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Record W4245201462 · doi:10.1111/fog.12482

Issue Information

2021· paratext· en· W4245201462 on OpenAlexaff
Steven J. Bograd, Martin Lindegren, Dtu Aqua, Jeff Rey Runge, Janet A. Nye, Shin‐ichi Ito, Michele Casini, Martín Castonguay, Ignacio A. Catalán, William W. L. Cheung, Sarah M. Glaser, Jon Grant, Roger Harris, Kazuhiko Hiramatsu, Alistair J. Hobday, Evan A. Howell, Zhang Chang, Akihide Kasai, Michio J. Kishi, Brian R. MacKenzie, H. Murase, Kaoru Nakata, Haruka Nishikawa, Ian Perry, Pierre Petitgas, Benjamin Planque, Jeffrey J. Polovina, Ryan R. Rykaczewski, Chiyuki Sassa, Lynne Shannon, Jonathan Sharples, Tetsuya Takatsu, Youngjun Tian, CD van der Lingen, Francisco Werner, Guoping Zhu

Bibliographic record

VenueFisheries Oceanography · 2021
Typeparatext
Languageen
FieldAgricultural and Biological Sciences
TopicFood Industry and Aquatic Biology
Canadian institutionsDalhousie UniversityUniversity of British ColumbiaFisheries and Oceans Canada
Fundersnot available
KeywordsCitationComputer scienceLibrary scienceInformation retrievalWorld Wide Web

Abstract

fetched live from OpenAlex

No abstract is available for this article.

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)0.001
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.950
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0010.000
Scholarly communication0.0050.003
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.9500.885

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 head0.017
GPT teacher head0.205
Teacher spread0.188 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2021
Admission routes1
Has abstractyes

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