Thank You to Our 2024 Reviewers
Bibliographic record
Abstract
Abstract On behalf of the journal, AGU, and the scientific community, we, the editors of Earth's Future, are delighted to publish the names of the 1,061 peer reviewers who provided 1,642 reviews for our journal in 2024 (italicized names have contributed three or more reviews). Your diligent efforts to provide timely comments on our submissions have significantly improved the manuscripts and elevated the scientific rigor of future research. As a unique transdisciplinary journal, Earth's Future delves into the state of the planet and its inhabitants, sustainable and resilient societies, the science of the Anthropocene, and predictions of our shared future through research articles, reviews, and commentaries. In the face of observed and anticipated global environmental and climatic changes, the need for high‐quality scientific theories, assessments, and projections about the future of our planet has never been more pressing. To safeguard research integrity in this crucial area, we rely on our reviewers' expertise and selfless cooperation. We extend our heartfelt thanks to each of the individuals listed below for their contributions to our journal and the broader scientific discourse. Your dedication is immensely appreciated.
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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 | 0.026 | 0.244 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.018 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.085 | 0.133 |
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".