Peer review filters and the incentives for high-risk research.
Seminar presented at the Santa Fe Institute "New Synthesis for the Science of Science" workshop, May 4-6 2022.
đáNh giá chất lượng nước sông nhuệ đáy chảy qua tỉnh nam định 6 tháng cuối nă...TÀI LIỆU NGÀNH MAY
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đáNh giá chất lượng nước sông nhuệ đáy chảy qua tỉnh nam định 6 tháng cuối nă...TÀI LIỆU NGÀNH MAY
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Trên cơ sở những tồn tại này, tác giả đã chọn đề tài “Nhận thức của cộng đồng địa phương về du lịch có trách nhiệm tại khu du lịch biển Sầm Sơn” làm nghiên
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Nghiên cứu quy trình sản xuất nước sâm từ các loại thảo dược quy mô phòng thí...TÀI LIỆU NGÀNH MAY
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The document discusses hypothesis testing and the scientific research process. It begins by defining a hypothesis as a tentative statement about the relationship between two or more variables that can be tested. It then outlines the typical steps in the scientific research process, which includes forming a question, background research, creating a hypothesis, experiment design, data collection, analysis, conclusions, and communicating results. Finally, it provides details on characteristics of a strong hypothesis, the process of hypothesis testing through statistical analysis, and setting up an experiment for hypothesis testing, including defining hypotheses, significance levels, sample size determination, and calculating standard deviation.
A chi-squared test (χ2) is basically a data analysis on the basis of observations of a random set of variables. Usually, it is a comparison of two statistical data sets. This test was introduced by Karl Pearson in 1900 for categorical data analysis and distribution. So, it was mentioned as Pearson’s chi-squared test.
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Xây dựng quy phạm thực hành vệ sinh thực phẩm haccp theo tcvn 5603-2008 (cac-...TÀI LIỆU NGÀNH MAY
Cá hộp Hichef Thái Lan
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Hotline: 070605.95.91
Giao hàng khu vực TP.HCM và đưa hàng ra chành đi các tỉnh.
Nhận viết luận văn Đại học , thạc sĩ - Zalo: 0917.193.864
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Nhận viết luận văn đại học, thạc sĩ trọn gói, chất lượng, LH ZALO=>0909232620
Tham khảo dịch vụ, bảng giá tại: https://vietbaitotnghiep.com/dich-vu-viet-thue-luan-van
Download luận văn thạc sĩ ngành khoa học môi trường với đề tài: Nghiên cứu áp dụng hệ thống quản lý môi trường theo tiêu chuẩn ISO 14001 tại công ty cổ phần Hóa dầu Petrolimex, cho các bạn làm luận văn tham khảo
Nhận thức của cộng đồng địa phương về du lịch có trách nhiệm tại khu du lịch ...luanvantrust
Trên cơ sở những tồn tại này, tác giả đã chọn đề tài “Nhận thức của cộng đồng địa phương về du lịch có trách nhiệm tại khu du lịch biển Sầm Sơn” làm nghiên
Để xem full tài liệu Xin vui long liên hệ page để được hỗ trợ
: https://www.facebook.com/thuvienluanvan01
HOẶC
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https://www.facebook.com/thuvienluanvan01
https://www.facebook.com/thuvienluanvan01
tai lieu tong hop, thu vien luan van, luan van tong hop, do an chuyen nganh
Nhận viết luận văn Đại học , thạc sĩ - Zalo: 0917.193.864
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https://www.facebook.com/thuvienluanvan01
https://www.facebook.com/thuvienluanvan01
tai lieu tong hop, thu vien luan van, luan van tong hop, do an chuyen nganh
Dịch vụ lập báo cáo môi trường ĐTM | Xử lý nước thải | 0903034381
Thiết kế quy hoạch
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Lập dự án
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http://lapduandautu.com.vn
Download luận văn đồ án tốt nghiệp ngành du lịch với đề tài: Tìm hiểu điều kiện phát triển loại hình du lịch mạo hiểm tại Hà Giang, cho các bạn làm luận văn tham khảo
Nghiên cứu quy trình sản xuất nước sâm từ các loại thảo dược quy mô phòng thí...TÀI LIỆU NGÀNH MAY
Để xem full tài liệu Xin vui long liên hệ page để được hỗ trợ
: https://www.facebook.com/thuvienluanvan01
HOẶC
https://www.facebook.com/garmentspace/
https://www.facebook.com/thuvienluanvan01
https://www.facebook.com/thuvienluanvan01
tai lieu tong hop, thu vien luan van, luan van tong hop, do an chuyen nganh
Nhận viết luận văn đại học, thạc sĩ trọn gói, chất lượng, LH ZALO=>0909232620
Tham khảo dịch vụ, bảng giá tại: https://vietbaitotnghiep.com/dich-vu-viet-thue-luan-van
Download luận văn thạc sĩ ngành hệ thống địa lí với đề tài: Ứng dụng GIS nghiên cứu ô nhiễm bụi ở thị xã Bỉm Sơn, tỉnh Thanh Hóa, cho các bạn làm luận văn tham khảo
Để xem full tài liệu Xin vui long liên hệ page để được hỗ trợ
: https://www.facebook.com/thuvienluanvan01
HOẶC
https://www.facebook.com/garmentspace/
https://www.facebook.com/thuvienluanvan01
https://www.facebook.com/thuvienluanvan01
tai lieu tong hop, thu vien luan van, luan van tong hop, do an chuyen nganh
Dịch vụ lập báo cáo môi trường ĐTM | Xử lý nước thải | 0918755356
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Lập dự án
Xử lý nước thải
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The document discusses hypothesis testing and the scientific research process. It begins by defining a hypothesis as a tentative statement about the relationship between two or more variables that can be tested. It then outlines the typical steps in the scientific research process, which includes forming a question, background research, creating a hypothesis, experiment design, data collection, analysis, conclusions, and communicating results. Finally, it provides details on characteristics of a strong hypothesis, the process of hypothesis testing through statistical analysis, and setting up an experiment for hypothesis testing, including defining hypotheses, significance levels, sample size determination, and calculating standard deviation.
A chi-squared test (χ2) is basically a data analysis on the basis of observations of a random set of variables. Usually, it is a comparison of two statistical data sets. This test was introduced by Karl Pearson in 1900 for categorical data analysis and distribution. So, it was mentioned as Pearson’s chi-squared test.
The Reproducibility Crisis in Psychological Science: One Year LaterJimGrange
The document discusses the reproducibility crisis in psychological science. It notes several cases from 2011 that called into question research practices. An open science collaboration attempted to replicate 100 psychology studies and found that only 36% replicated. The document recommends promoting open science practices like replication, transparent statistics and data sharing, and teaching/rewarding rigor over quantity or novelty. It argues the field needs to change incentives to prioritize accuracy over publication. Overall, the document analyzes issues compromising reproducibility and proposes open science solutions to improve research integrity in psychology.
1.1 why research
1.2 a brief history of SC
1.2 So what is SC ? Why the fuck I care
1.2 Induction
1.2 Popper and the need for falsifiability
1.2 but what separates SC non-SC (NSC) for Popper ?
1.2 The failing of falsification and Bayesianism
1.2 The hypothetico-deductive method .
1.3 Quantitative VS Qualitative
1.5 Planning research
D. Mayo: Replication Research Under an Error Statistical Philosophy jemille6
D. Mayo (Virginia Tech) slides from her talk June 3 at the "Preconference Workshop on Replication in the Sciences" at the 2015 Society for Philosophy and Psychology meeting.
The document discusses empirical research and different approaches to obtaining knowledge. It defines empirical research as providing a scientific means of obtaining knowledge through objective observation and verification. It also discusses the rationalistic mode of obtaining knowledge through strict adherence to logic and the scientific method. Scientific knowledge is characterized as being transmissible, cumulative, and generalizable.
D. Mayo: Philosophy of Statistics & the Replication Crisis in Sciencejemille6
D. Mayo discusses various disputes-notably the replication crisis in science-in the context of her just released book: Statistical Inference as Severe Testing: How to Get Beyond the Statistics Wars.
This document provides guidance on how to read, analyze, and critique a scientific study. It discusses key concepts like the null hypothesis, statistical significance, and types of data and appropriate statistical tests. It also outlines important steps to follow when reviewing a study, including understanding the study design, evaluating the data collection and analysis, interpreting graphs and statistics, and carefully considering the discussion and conclusions. Finally, it identifies several common pitfalls to watch out for related to statistical analysis and presentation of results.
The document discusses hypothesis testing and statistical inference. It defines key terms like hypothesis, null hypothesis, alternative hypothesis, parameters, statistics, population, sample, parametric tests, and significance level. It explains that the goal of hypothesis testing is to either confirm or disconfirm a research hypothesis by testing the null hypothesis. The process involves collecting a sample, calculating statistics, determining p-values and confidence levels, and deciding whether to reject or fail to reject the null hypothesis based on these values. The document also discusses types of errors like type I and type II errors that can occur in hypothesis testing.
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D. G. Mayo: Your data-driven claims must still be probed severelyjemille6
In the session "Philosophy of Science and the New Paradigm of Data-Driven Science at the American Statistical Association Conference on Statistical Learning and Data Science/Nonparametric Statistics
This document discusses the traditional problem of induction and attempts to justify the inductive method. It presents Hume's view that induction cannot be justified since we cannot infer general laws from specific cases. Two options are considered: obtain knowledge non-inductively or accept induction is irrational. Popper argues for the first option, proposing scientific theories are conjectures subject to falsification, not verification. He claims induction is not needed if we tentatively accept the best theories until falsified. While this avoids Hume's problem, critics argue falsifiability is too weak a criterion and background assumptions are needed for tests. Overall, the document examines Hume's skepticism of induction and Popper's attempt to justify scientific reasoning without relying on induction
Hypothesis testing involves stating a null hypothesis (H0) and an alternative hypothesis (H1). H0 assumes there is no effect or relationship in the population. H1 states there is an effect. A study is conducted and statistics are used to determine if the data supports rejecting H0 in favor of H1. The p-value indicates the probability of obtaining results as extreme as the observed data or more extreme if H0 is true. If p ≤ the predetermined significance level (α = 0.05), H0 is rejected in favor of H1. Otherwise, H0 is retained but not proven true. Type I and II errors can occur when the true hypothesis is incorrectly rejected or retained.
hypothesis-Meaning need for hypothesis qualities of good hypothesis type of hypothesis null and alternative hypothesis sources of hypothesis formulation of hypothesis, hypothesis testing
The Statistics Wars: Errors and Casualtiesjemille6
ABSTRACT: Mounting failures of replication in the social and biological sciences give a new urgency to critically appraising proposed statistical reforms. While many reforms are welcome (preregistration of experiments, replication, discouraging cookbook uses of statistics), there have been casualties. The philosophical presuppositions behind the meta-research battles remain largely hidden. Too often the statistics wars have become proxy wars between competing tribe leaders, each keen to advance one or another tool or school, rather than build on efforts to do better science. Efforts of replication researchers and open science advocates are diminished when so much attention is centered on repeating hackneyed howlers of statistical significance tests (statistical significance isn’t substantive significance, no evidence against isn’t evidence for), when erroneous understanding of basic statistical terms goes uncorrected, and when bandwagon effects lead to popular reforms that downplay the importance of error probability control. These casualties threaten our ability to hold accountable the “experts,” the agencies, and all the data handlers increasingly exerting power over our lives.
D. Mayo: Philosophical Interventions in the Statistics Warsjemille6
ABSTRACT: While statistics has a long history of passionate philosophical controversy, the last decade especially cries out for philosophical illumination. Misuses of statistics, Big Data dredging, and P-hacking make it easy to find statistically significant, but spurious, effects. This obstructs a test's ability to control the probability of erroneously inferring effects–i.e., to control error probabilities. Disagreements about statistical reforms reflect philosophical disagreements about the nature of statistical inference–including whether error probability control even matters! I describe my interventions in statistics in relation to three events. (1) In 2016 the American Statistical Association (ASA) met to craft principles for avoiding misinterpreting P-values. (2) In 2017, a "megateam" (including philosophers of science) proposed "redefining statistical significance," replacing the common threshold of P ≤ .05 with P ≤ .005. (3) In 2019, an editorial in the main ASA journal called for abandoning all P-value thresholds, and even the words "significant/significance".
A word on each. (1) Invited to be a "philosophical observer" at their meeting, I found the major issues were conceptual. P-values measure how incompatible data are from what is expected under a hypothesis that there is no genuine effect: the smaller the P-value, the more indication of incompatibility. The ASA list of familiar misinterpretations–P-values are not posterior probabilities, statistical significance is not substantive importance, no evidence against a hypothesis need not be evidence for it–I argue, should not be the basis for replacing tests with methods less able to assess and control erroneous interpretations of data. (Mayo 2016, 2019). (2) The "redefine statistical significance" movement appraises P-values from the perspective of a very different quantity: a comparative Bayes Factor. Failing to recognize how contrasting approaches measure different things, disputants often talk past each other (Mayo 2018). (3) To ban P-value thresholds, even to distinguish terrible from warranted evidence, I say, is a mistake (2019). It will not eradicate P-hacking, but it will make it harder to hold P-hackers accountable. A 2020 ASA Task Force on significance testing has just been announced. (I would like to think my blog errorstatistics.com helped.)
To enter the fray between rival statistical approaches, it helps to have a principle applicable to all accounts. There's poor evidence for a claim if little if anything has been done to find it flawed even if it is. This forms a basic requirement for evidence I call the severity requirement. A claim passes with severity only if it is subjected to and passes a test that probably would have found it flawed, if it were. It stems from Popper, though he never adequately cashed it out. A variant is the frequentist principle of evidence developed with Sir David Cox (Mayo and Cox 20
1) The document discusses philosophical interventions in statistical debates and proposed reforms. It outlines three types of interventions: illuminating debates within statistics, reformulating frequentist tools through a severity perspective, and scrutinizing proposed reforms from the replication crisis.
2) A key idea is that evidence for a claim only comes when it has undergone severe testing, meaning it would probably have failed if it were false. The document argues for keeping the best aspects of Fisherian and Neyman-Pearson testing through a severity interpretation.
3) In scrutinizing reforms, it questions proposals to abandon statistical significance and P-value thresholds, arguing this could exacerbate selective reporting instead of reducing it. The document advocates reformulating tests
The cost of acquiring information by natural selectionCarl Bergstrom
This is a short talk that I gave at the Banff International Research Station workshop on Modeling and Theory in Population Biology. The idea is to try to understand how the burden of natural selection relates to the amount of information that selection puts into the genome.
It's based on the first part of this research paper:
The cost of information acquisition by natural selection
Ryan Seamus McGee, Olivia Kosterlitz, Artem Kaznatcheev, Benjamin Kerr, Carl T. Bergstrom
bioRxiv 2022.07.02.498577; doi: https://doi.org/10.1101/2022.07.02.498577
Transforming Science Education in An Age of MisinformationCarl Bergstrom
Keynote at theNorthwest Commission on Colleges and Universities annual meeting. I argue that if we want to address science misinformation on social media and beyond, we need to teach (1) data reasoning and (2) an understanding of the social process of science.
Proactive COVID-19 testing to mitigate spreadCarl Bergstrom
1. Carl Bergstrom discusses the role of proactive COVID-19 testing in mitigating the spread of the virus. He outlines four roles for testing: individual diagnosis, clearance, surveillance, and mitigation.
2. Bergstrom presents analytic models showing that regular testing can significantly reduce exposure days by identifying infectious individuals who would otherwise spread the virus without knowing they are infected. The models factor in testing frequency, false negative rates, and delays in getting results.
3. More complex stochastic models developed by Ryan McGee capture real-world factors like disease dynamics across social networks, individual heterogeneity, and varying test sensitivity over time. Simulations show that even with these complexities, regular testing as part of a test-trace
Burning Money: Competition Generates Massive Inefficiencies in the Grant Pr...Carl Bergstrom
Talk presented at the Philosophy of Science Association annual conference, November 4th, 2018, Seattle WA.
Based on the following paper: https://arxiv.org/abs/1804.03732
Department seminar, Princeton Ecology and Evolutionary Biology. October 21, 2016.
Discussing two working papers: https://arxiv.org/abs/1609.00494 and https://arxiv.org/abs/1605.05822
Why scientists chase big problems (and why it matters)Carl Bergstrom
1) The model examines how scientists allocate research effort across problems based on incentives from contemporary scientific institutions. It models scientists racing to solve "hot problems" that are well-defined, important, and pull scientists from other work.
2) The model finds there is always a Nash equilibrium where some scientists pursue the problem while others opt out. The unique risk dominant equilibrium has scientists with the highest scientific capital pursue the problem.
3) Allowing scientists to publish partial results can recruit more effort to problems by having more scientists work on early stages, though it does not necessarily accelerate overall problem solving. Social welfare is maximized when scientists with the highest capital work on problems.
Citation metrics and the stories they tellCarl Bergstrom
1. The document summarizes research presented at an international symposium on citation networks and how they can be analyzed to understand the development of science.
2. Key topics included using algorithms like Eigenfactor to rank the importance of papers, journals, and authors based on citation patterns and developing network clustering methods to identify modules and hierarchies in large citation networks.
3. Research was presented on applying these techniques to study gender disparities in academic publishing and the emergence of new fields like neuroscience.
It's all a game: The twin fallacies of epistemic purity and the scholarly inv...Carl Bergstrom
My talk from the Feb 2016 Gaming Metrics workshop at UC Davis.
Video of the talk at http://bit.ly/1WwfMxY (begins 20:00)
For some reason the visual gradients (used to indicate gradients of behavior) have not rendered here.
Anthropogenic evolution, externalities, and public healthCarl Bergstrom
Slides from a departmental seminar in UCLA's department of Ecology and Evolutionary Biology, January 6th 2016.
The seminar was videotaped and should be available shortly online.
Navigating science using citation networksCarl Bergstrom
Carl Bergstrom's presentation from Microsoft's Big Scholarly Data workshop held in Redmond 7/10/2015. The presentation makes the argument that the scholarly community is far too dependent on Google Scholar for the vital task of searching the scientific literature. Google scholar uses an unknown algorithm on an unknown corpus and allows neither extension nor community development. Moreover, it may not persist forever. As a step toward a solution, I propose that the scholarly community take charge of its output. Creating an open repository of full text for all of scientific publication faces copyright issues that will be intractable in the short term, but creating an open citation graph for all of science is feasible today. At the Eigenfactor Project, we specialize in using citation networks to reveal the scientific landscape. I provide a series of quick illustrations of the kinds of things we can do with large-scale citation data.
(June 12, 2024) Webinar: Development of PET theranostics targeting the molecu...Scintica Instrumentation
Targeting Hsp90 and its pathogen Orthologs with Tethered Inhibitors as a Diagnostic and Therapeutic Strategy for cancer and infectious diseases with Dr. Timothy Haystead.
EWOCS-I: The catalog of X-ray sources in Westerlund 1 from the Extended Weste...Sérgio Sacani
Context. With a mass exceeding several 104 M⊙ and a rich and dense population of massive stars, supermassive young star clusters
represent the most massive star-forming environment that is dominated by the feedback from massive stars and gravitational interactions
among stars.
Aims. In this paper we present the Extended Westerlund 1 and 2 Open Clusters Survey (EWOCS) project, which aims to investigate
the influence of the starburst environment on the formation of stars and planets, and on the evolution of both low and high mass stars.
The primary targets of this project are Westerlund 1 and 2, the closest supermassive star clusters to the Sun.
Methods. The project is based primarily on recent observations conducted with the Chandra and JWST observatories. Specifically,
the Chandra survey of Westerlund 1 consists of 36 new ACIS-I observations, nearly co-pointed, for a total exposure time of 1 Msec.
Additionally, we included 8 archival Chandra/ACIS-S observations. This paper presents the resulting catalog of X-ray sources within
and around Westerlund 1. Sources were detected by combining various existing methods, and photon extraction and source validation
were carried out using the ACIS-Extract software.
Results. The EWOCS X-ray catalog comprises 5963 validated sources out of the 9420 initially provided to ACIS-Extract, reaching a
photon flux threshold of approximately 2 × 10−8 photons cm−2
s
−1
. The X-ray sources exhibit a highly concentrated spatial distribution,
with 1075 sources located within the central 1 arcmin. We have successfully detected X-ray emissions from 126 out of the 166 known
massive stars of the cluster, and we have collected over 71 000 photons from the magnetar CXO J164710.20-455217.
Describing and Interpreting an Immersive Learning Case with the Immersion Cub...Leonel Morgado
Current descriptions of immersive learning cases are often difficult or impossible to compare. This is due to a myriad of different options on what details to include, which aspects are relevant, and on the descriptive approaches employed. Also, these aspects often combine very specific details with more general guidelines or indicate intents and rationales without clarifying their implementation. In this paper we provide a method to describe immersive learning cases that is structured to enable comparisons, yet flexible enough to allow researchers and practitioners to decide which aspects to include. This method leverages a taxonomy that classifies educational aspects at three levels (uses, practices, and strategies) and then utilizes two frameworks, the Immersive Learning Brain and the Immersion Cube, to enable a structured description and interpretation of immersive learning cases. The method is then demonstrated on a published immersive learning case on training for wind turbine maintenance using virtual reality. Applying the method results in a structured artifact, the Immersive Learning Case Sheet, that tags the case with its proximal uses, practices, and strategies, and refines the free text case description to ensure that matching details are included. This contribution is thus a case description method in support of future comparative research of immersive learning cases. We then discuss how the resulting description and interpretation can be leveraged to change immersion learning cases, by enriching them (considering low-effort changes or additions) or innovating (exploring more challenging avenues of transformation). The method holds significant promise to support better-grounded research in immersive learning.
Immersive Learning That Works: Research Grounding and Paths ForwardLeonel Morgado
We will metaverse into the essence of immersive learning, into its three dimensions and conceptual models. This approach encompasses elements from teaching methodologies to social involvement, through organizational concerns and technologies. Challenging the perception of learning as knowledge transfer, we introduce a 'Uses, Practices & Strategies' model operationalized by the 'Immersive Learning Brain' and ‘Immersion Cube’ frameworks. This approach offers a comprehensive guide through the intricacies of immersive educational experiences and spotlighting research frontiers, along the immersion dimensions of system, narrative, and agency. Our discourse extends to stakeholders beyond the academic sphere, addressing the interests of technologists, instructional designers, and policymakers. We span various contexts, from formal education to organizational transformation to the new horizon of an AI-pervasive society. This keynote aims to unite the iLRN community in a collaborative journey towards a future where immersive learning research and practice coalesce, paving the way for innovative educational research and practice landscapes.
Authoring a personal GPT for your research and practice: How we created the Q...Leonel Morgado
Thematic analysis in qualitative research is a time-consuming and systematic task, typically done using teams. Team members must ground their activities on common understandings of the major concepts underlying the thematic analysis, and define criteria for its development. However, conceptual misunderstandings, equivocations, and lack of adherence to criteria are challenges to the quality and speed of this process. Given the distributed and uncertain nature of this process, we wondered if the tasks in thematic analysis could be supported by readily available artificial intelligence chatbots. Our early efforts point to potential benefits: not just saving time in the coding process but better adherence to criteria and grounding, by increasing triangulation between humans and artificial intelligence. This tutorial will provide a description and demonstration of the process we followed, as two academic researchers, to develop a custom ChatGPT to assist with qualitative coding in the thematic data analysis process of immersive learning accounts in a survey of the academic literature: QUAL-E Immersive Learning Thematic Analysis Helper. In the hands-on time, participants will try out QUAL-E and develop their ideas for their own qualitative coding ChatGPT. Participants that have the paid ChatGPT Plus subscription can create a draft of their assistants. The organizers will provide course materials and slide deck that participants will be able to utilize to continue development of their custom GPT. The paid subscription to ChatGPT Plus is not required to participate in this workshop, just for trying out personal GPTs during it.
The debris of the ‘last major merger’ is dynamically youngSérgio Sacani
The Milky Way’s (MW) inner stellar halo contains an [Fe/H]-rich component with highly eccentric orbits, often referred to as the
‘last major merger.’ Hypotheses for the origin of this component include Gaia-Sausage/Enceladus (GSE), where the progenitor
collided with the MW proto-disc 8–11 Gyr ago, and the Virgo Radial Merger (VRM), where the progenitor collided with the
MW disc within the last 3 Gyr. These two scenarios make different predictions about observable structure in local phase space,
because the morphology of debris depends on how long it has had to phase mix. The recently identified phase-space folds in Gaia
DR3 have positive caustic velocities, making them fundamentally different than the phase-mixed chevrons found in simulations
at late times. Roughly 20 per cent of the stars in the prograde local stellar halo are associated with the observed caustics. Based
on a simple phase-mixing model, the observed number of caustics are consistent with a merger that occurred 1–2 Gyr ago.
We also compare the observed phase-space distribution to FIRE-2 Latte simulations of GSE-like mergers, using a quantitative
measurement of phase mixing (2D causticality). The observed local phase-space distribution best matches the simulated data
1–2 Gyr after collision, and certainly not later than 3 Gyr. This is further evidence that the progenitor of the ‘last major merger’
did not collide with the MW proto-disc at early times, as is thought for the GSE, but instead collided with the MW disc within
the last few Gyr, consistent with the body of work surrounding the VRM.
ESA/ACT Science Coffee: Diego Blas - Gravitational wave detection with orbita...Advanced-Concepts-Team
Presentation in the Science Coffee of the Advanced Concepts Team of the European Space Agency on the 07.06.2024.
Speaker: Diego Blas (IFAE/ICREA)
Title: Gravitational wave detection with orbital motion of Moon and artificial
Abstract:
In this talk I will describe some recent ideas to find gravitational waves from supermassive black holes or of primordial origin by studying their secular effect on the orbital motion of the Moon or satellites that are laser ranged.
Current Ms word generated power point presentation covers major details about the micronuclei test. It's significance and assays to conduct it. It is used to detect the micronuclei formation inside the cells of nearly every multicellular organism. It's formation takes place during chromosomal sepration at metaphase.
1. A New Synthesis for
the Science of Science
May 4-6, 2022
Peer review filters
and the incentives
for high-risk research
Carl T. Bergstrom
Photo: Carl Bergstrom
2. Peer review filters and the
incentives for high-risk
research
Carl T. Bergstrom
Department of Biology
University of Washington
Kevin Gross
Department of Statistics
North Carolina State University
Kevin Gross, NCSU
A New Synthesis for the Science of Science
May 4-6, 2022
11. Ideas Proposals Experiments Manuscripts Publications
Ex post vs. ex ante review
Grant
peer review
Manuscript
peer review
Ex ante
review:
Results are
unknown
Ex post
review:
Results are
known
12. Funding agencies want to support useful research.
Journals want to publish useful research.
Photo: Carl Bergstrom
13. Funding agencies want to support useful research.
Journals want to publish useful research.
But what defines useful ?
Photo: Carl Bergstrom
14. With all else equal, a scientific result is useful to the degree
that it changes beliefs about some epistemic object*.
*A hypothesis, model, claim, parameter value, etc.
Photo: Carl Bergstrom
15. Informal Justification:
If it doesn’t change anyone’s
beliefs, what was the point of
you doing it?
What is the point of me
reading about it?
Photo: Carl Bergstrom
17. A scoring rule quantifies how well a
probabilistic prediction anticipates an
outcome x. Lower scores are better.
A proper scoring rule ensures that a
forecaster maximizes her expected
score by reporting her true beliefs.
Formal Apparatus:
Forecasting theory.
Photo: Carl Bergstrom
18. How well do I understand something?
“How well can I predict the outcomes
of observable events or the truth of
empirical propositions?”
Scientists aim to develop explanatorily-
adequate models of physical phenomena.
Photo: Carl Bergstrom
19. Scoring rules are useful for quantifying
scientific progress because they reward
movement towards beliefs that successfully
anticipate observable phenomena, while also
rewarding faithful awareness of uncertainty.
Both are key virtues in science.
Photo: Carl Bergstrom
20. Example: Is there life on Europa?
Suppose I believe there is a 30% chance that
there is life there (x=1) and 70% there is not
(x=0). I predict p=0.3.
Use a scoring rule known as the Brier score:
S(p,x)=(x-p)2
If there is life on Europa, S=(1-0.3) 2 =0.49.
If there is not life there, S=(1-0.3) 2 =0.09.
Photo: Carl Bergstrom
21. Suppose a new observation (y)
leads me to update my belief in
life on Europa from 30% to 50%.
What is the value of that observation to me?
With the scoring rule
framework, the value is the
expected increase in my score
resulting from this update.
Photo: Carl Bergstrom
22. For a scoring rule S, the cost of using probabilities p to forecast,
when the real probabilities are q, is the divergence d(q||p).
So if based on an observation y I update my beliefs p to q(p,y), I
expect this to improve my score by d(q(p,y)||p).
Photo: Carl Bergstrom
23. Example: Is there life on Europa?
After observing y, I update my beliefs from p=0.3 to p=0.5.
The value of this is the divergence* d(0.5||0.3) = 0.2.
* Divergence under the Brier score is just the change in probability assignment.
Photo: Carl Bergstrom
24. So that’s the ex-post value of an experiment in the scoring rule framework:
The divergence between prior and posterior after observing the outcome.
Photo: Carl Bergstrom
25. Cool digression: A Shannon information scoring rule.
If instead of using the Brier score
S(P
,x)=(x-px)2 we use the ignorance
score (or surprisal) S(P
, x) = -Log(px),
the divergence is the familiar
Kullback-Leibler divergence.
Photo: Carl Bergstrom
26. Given Bayesian updating and a proper scoring rule,
the greater the change in beliefs, the greater the
value of updating.
Photo: Carl Bergstrom
27. Not all research is
basic science.
Sometimes you
have problems you
want to solve.
Then predicting
arbitrary things may
not be a good
measure of value.
Photo: Carl Bergstrom
28. It’s ok.
We can always define the value of an experiment —
for any decision problem we want.
Photo: Carl Bergstrom
29.
30. Frankel and Kamenica:
Every decision problem induces a measure of information/uncertainty
with the properties below
Every information measure with these properties corresponds to a
decision problem.
1. The information from an observation is always non-negative
2. Observations that do not change beliefs carry no information
3. The order in which observations are made do not on average
change the total amount of information obtained.
31. To summarize, any decision problem induces
an information measure such that the more
information you acquire, the better.
32. When the decision problem involves
prediction, the more it makes you
change your beliefs, the more
valuable the information.
33. With all else equal, a scientific result is useful to the degree
that it changes beliefs about some epistemic object*.
*A hypothesis, model, claim, parameter value, etc.
Photo: Carl Bergstrom
34. Thus far we have considered the
ex post value of an experiment.
But we often have to make make
decisions on what to do ex ante.
35. So what is the value of an experiment, ex ante?
Simple: The expected ex post value, given beliefs.
Photo: Carl Bergstrom
36. Cool digression: Shannon
information scoring rule.
If we use the S(P
, x) = -Log(px),
ex ante value of an experiment
is the mutual information
between what an investigator
believes will happen and what
will actually happen.
Photo: Carl Bergstrom
37. Whose beliefs do we use
to set these probabilities?
Whose beliefs do we measure the shift in?
38. What is the ex ante value of an experiment
to an investigator?
39. Example: binary hypothesis test.
Experiment returns an observationY drawn from a normal distribution
with mean μ0 if H0 is true and mean μ0 if H0 is true.
40. Example: binary hypothesis test.
Experiment returns an observationY drawn from a normal distribution
with mean μ0 if H0 is true and mean μ0 if H0 is true.
41. The most useful experiment to the
investigator is the most uncertain one.
But that’s the value of the result to
the investigator themselves.
To get published, the investigator needs to come up
with a result that is useful to their peers, not to
themself.
42. The investigator can leverage their private
beliefs.
Investigator’s probabilities
Reviewers’ probabilities
43. The investigator can leverage their private
beliefs.
Investigator’s probabilities
Reviewers’ probabilities
44. The investigator should pick
an experiment where their
expectations different from
that of the community.
But not one that the
community will consider wildly
improbable, and just ignore.
45. Ex ante review doesn’t allow the investigator
to leverage private beliefs.
The reason is that the reviewers don’t know the result of the
experiment when they evaluate the merits of conducting it.
Reviewers never expect to be surprised, ex ante.
Ex ante, investigators have to propose experiments that
reviewers think will work.
46. Ideas Proposals Experiments Manuscripts Publications
Ex post vs. ex ante review
Grant
peer review
Manuscript
peer review
Investigator wants an
experiment the reviewers
expect will be useful
Investigator wants an
experiment that reviewers will
find surprising (and thus
probably would not have
expected to be useful ex
48. Shared uncertainty vs. scientific controversy
Shared Uncertainty: Everyone agrees it’s a 50-50 proposition.
Scientific Controversy: Half of the reviewers think it’s almost certain in
one direction, half think it’s almost certain in the other.
Which do ex ante reviewers prefer to resolve?
49. Shared uncertainty vs. scientific controversy
In a scientific controversy, no one expects to be surprised and thinks
private value is low. But they expect the experiment will be highly
informative to their opponents and thus public value is high.
In a shared uncertainty, everyone expects the result to be somewhat
information to everyone. Public and private value are both moderate.
Private value favors shared uncertainty.
Public value favors scientific controversy.
50. Simulation: problem selection
Large population of binary hypotheses H.
Large population of investigators I.
Each investigator has belief IH about the probability of hypothesis H.
We simulate 50 investigators.
Each investigator samples 15 hypotheses and, knowing the
distribution of others’ beliefs chooses to test the one with the highest
expected value.
51. Ex post review
If investigators are concerned only
about ex-post review they would like
to test hypotheses they find likely but
others uniformly consider unlikely but
not impossible.
Such hypotheses are thin on the
ground and so investigators do the
best they can.
Mean community belief in hypothesis
favored by the investigator
SD
belief
in
hypothesis
52. Ex ante review: private value
With ex ante review by reviewers
concerned with private value,
investigators seek experiments with
mean belief around 50% and minimal
heterogeneity
Investigators can find something close
to this and do so.
Mean community belief in hypothesis
favored by the investigator
SD
belief
in
hypothesis
53. Ex ante review: public value
With ex ante review by reviewers
concerned with private value,
investigators seek experiments with
mean belief around 50% and
substantial heterogeneity.
Investigators can find something close
to this and do so.
Mean community belief in hypothesis
favored by the investigator
SD
belief
in
hypothesis
54. Proposition
Given any proper scoring rule S, an average belief P about some
proposition, and a proposal for a definitive experiment E:
1. If reviewers value the proposal according to what they will learn
themselves, any heterogeneity in beliefs will decrease their ex ante
valuation of E.
2. If reviewers value the proposal according to what others will learn,
any heterogeneity in beliefs will increase their ex ante valuation of E.
55. Implications
Experiments are valuable to the
degree that they change beliefs.
We can formalize this intuition
using the theory of scoring rules.
Photo: Carl Bergstrom
56. Implications
Investigators can leverage private beliefs in
ex post review, to choose experiments they
consider likely to surprise their colleagues.
In ex ante review, investigators can’t. They
have to choose experiments that reviewers
consider likely to be informative.
This creates a tension between the two
filter stages.
Photo: Carl Bergstrom
57. Implications
Because grants are reviewed ex
ante, researchers cannot afford
to submit risky grant proposals
— reviewers expect them to fail.
Other modes of ex ante review,
such as registered reports, may
similarly discourage risky work.
Photo: Carl Bergstrom
58. Implications
Ex ante reviewers will favor
resolving shared uncertainties if
they focus on how much they
expect to be surprised
themselves.
They will favor resolving scientific
controversies if they focus on
how much the experiment will
change the beliefs of others.
Photo: Carl Bergstrom
59. Implications
Photo: Carl Bergstrom
Funding agencies can steer the outcome by
telling reviewers to consider their own value,
or the value to the community. Grant panel
discussions may help with the latter.