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Record W4376140397 · doi:10.1002/ece3.9961

Reproducibility in ecology and evolution: Minimum standards for data and code

2023· article· en· W4376140397 on OpenAlexaff
Gareth Jenkins, Andrew P. Beckerman, Céline Bellard, Ana Benítez‐López, Aaron M. Ellison, Christopher G. Foote, Andrew L. Hufton, Marcus A. Lashley, Christopher J. Lortie, Zhaoxue Ma, Allen J. Moore, Shawn R. Narum, Johan Nilsson, Bridget O’Boyle, Diogo B. Provete, Orly Razgour, Loren H. Rieseberg, Cynthia Riginos, Luca Santini, Benjamin Sibbett, Pedro R. Peres‐Neto

Bibliographic record

VenueEcology and Evolution · 2023
Typearticle
Languageen
FieldComputer Science
TopicResearch Data Management Practices
Canadian institutionsConcordia UniversityUniversity of British ColumbiaYork University
Fundersnot available
KeywordsCommitComputer scienceData scienceCitationOpen scienceSimple (philosophy)Code (set theory)Open dataEcologyWorld Wide WebBiologyDatabaseProgramming languageEpistemologyMathematics

Abstract

fetched live from OpenAlex

We call for journals to commit to requiring open data be archived in a format that will be simple and clear for readers to understand and use. If applied consistently, these requirements will allow contributors to be acknowledged for their work through citation of open data, and facilitate scientific progress.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.532
metaresearch head score (Gemma)0.731
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Reproducibility · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.468
Threshold uncertainty score0.577

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.5320.731
Meta-epidemiology (narrow)0.0020.005
Meta-epidemiology (broad)0.0070.006
Bibliometrics0.0210.020
Science and technology studies0.0100.019
Scholarly communication0.0310.027
Open science0.0180.019
Research integrity0.0140.025
Insufficient payload (model declined to judge)0.0090.023

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.102
GPT teacher head0.390
Teacher spread0.288 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designTheoretical or conceptual
DomainReproducibility
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations69
Published2023
Admission routes1
Has abstractyes

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