Appreciation of Peer Reviewers for 2014
Bibliographic record
Abstract
Scientific publishing relies heavily on the process of peer review to ensure that the most well‐founded and clearly described science is communicated to our colleagues and beyond. Papers published in JGR Planets benefit from the time, effort, and talents of the reviewers who provide thoughtfully considered advice on each manuscript. This role is critical to the advancement of planetary science as these reviews lead to even clearer and higher‐quality papers. In 2014, papers submitted to JGR Planets were the beneficiaries of more than 520 reviews provided by 340 members of the planetary science community. To everyone who volunteers their time and intellect to peer reviewing, thank you for helping us share the best science possible.
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 | 0.129 | 0.521 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.003 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.040 | 0.013 |
| Open science | 0.008 | 0.010 |
| Research integrity | 0.017 | 0.018 |
| Insufficient payload (model declined to judge) | 0.215 | 0.299 |
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".