Thank You to Our 2024 Peer Reviewers
Bibliographic record
Abstract
Abstract We are very grateful for the reviews done in 2024 to support the published articles of Perspectives of Earth and Space Sciences. This year we had 40 reviews. As a relatively young journal, Perspectives is still defining its role withing AGU. Perspectives has added several new article formats in order to help support intra‐AGU communication. These new formats included Commentaries, Opinions, News Items, and Memorials, which means additional challenges for reviewers, as the review criteria for these new formats vary from each other. This year also saw an increase in the diversity of styles and authorships of Perspectives Articles, which are the primary format of the journal, but the bulk of these Articles still took a large‐scale big‐picture view of a particular scientific perspective, across the full range of Earth and space sciences. Once again this year, we are very grateful for the wisdom and flexibility shown by our reviewers, and the entire editorial board of Perspectives would like to express our deep appreciation for all the work they have done. Thank you!
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 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 | 0.038 | 0.296 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.018 | 0.008 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.091 | 0.139 |
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".