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Record W4395670328 · doi:10.1029/2024jg008197

Recognizing and Appreciating the 2023 <i>JGR</i>: <i>Biogeosciences</i> Reviewers

2024· article· en· W4395670328 on OpenAlexaff
Ben Bond‐Lamberty, Xiaojuan Feng, D. N. Huntzinger, Marguerite A. Xenopoulos

Bibliographic record

VenueJournal of Geophysical Research Biogeosciences · 2024
Typearticle
Languageen
FieldComputer Science
TopicResearch Data Management Practices
Canadian institutionsTrent University
Fundersnot available
KeywordsPsychology

Abstract

fetched live from OpenAlex

Abstract Peer review is a crucial but time‐consuming and sometimes frustrating part of science. The Editors of JGR: Biogeosciences are deeply appreciative of, and sincerely thank, the many 2023 reviewers who donated their time and expertise in support of the journal.

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)0.059
metaresearch head score (Gemma)0.288
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.941
Threshold uncertainty score0.314

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0590.288
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0060.008
Science and technology studies0.0030.004
Scholarly communication0.0250.011
Open science0.0040.006
Research integrity0.0060.010
Insufficient payload (model declined to judge)0.0280.061

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 head0.145
GPT teacher head0.414
Teacher spread0.269 · 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
DomainEvaluation
GenreCommentary

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
Published2024
Admission routes1
Has abstractyes

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