Thank You to Our 2023 Reviewers
Bibliographic record
Abstract
Abstract The entire editorial board of the Journal of Geophysical Research‐Solid Earth would like to sincerely thank all our colleagues who reviewed manuscripts for us in 2023. The hours they spent reading in order to provide insightful comments on manuscripts not only help improve the quality of these manuscripts but also ensure the scientific rigor of our reviewing process and eventually, of the research published in the field of Solid Earth Geophysics by our journal. With the advent of open science and AGU's data policy, the reviewing process now also encompasses checking the accessibility and availability of data and developed software. This is a key objective of AGU's FAIR (Findable, Accessible, Interoperable and Reusable) policy, for which many reviewers have provided suggestions that helped to improve the data presentation and availability, and which also fed the editorial board's reflection on the matter. Of course, we particularly appreciate timely reviews, particularly in light of the growing demands imposed by the increase of manuscripts submitted to Journal of Geophysical Research‐Solid Earth. We received 1,869 submissions in 2023, and 1,472 reviewers contributed to their evaluation by providing 2,237 reviews in total. We are deeply thankful for all of their contributions. The editorial board of Journal of Geophysical Research‐Solid Earth: Rachel Abercrombie, Yves Bernabé, Michael Bostock (former editor), Mark Dekkers, Anke Friedrich, Shin‐Chan Han, Satoshi Ide, Isabelle Manighetti (former EIC), Fenglin Niu, Douglas R. Schmitt, Alexandre Schubnel (EIC), Jun Tsuchiya, and all the associate editors of JGR‐SE.
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 | 0.022 | 0.151 |
| 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 | 0.022 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.120 | 0.175 |
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".