Thank You to Our 2024 Reviewers
Bibliographic record
Abstract
Abstract The editors of Tectonics would like to offer our sincere thanks to those who reviewed manuscripts for us in 2024. The time reviewers spend reading and commenting on manuscripts helps to ensure that our science is well communicated, clearly documented, appropriately placed in the context of prior work, and effectively archived for future usage. We understand that as one of the AGU journals hosting long format articles, reviewing a manuscript for Tectonics is a considerable time commitment. For this reason, we are particularly grateful to our reviewers for their diligence in helping to provide high‐quality, timely reviews. We also appreciate the efforts many reviewers contribute toward advancing open science by evaluating the availability and accessibility of data, which are key objectives of the AGU's FAIR data policy. The papers published in Tectonics in 2024 benefitted from the careful scrutiny and constructive critique drawn from the expertise of 493 reviewers, who provided a total of 706 reviews. We are grateful for this contribution toward producing the high‐quality output that has helped Tectonics maintain a prominent position in scientific publishing for decades, and for the spirit of teamwork that makes the peer review process an asset in our community.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.030 | 0.273 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.018 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.075 | 0.125 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".