Thank You to Our 2023 Reviewers
Bibliographic record
Abstract
Geochemistry, Geophysics, Geosystems (G-Cubed) is a top-rated Earth science journal (3.62 Web of Science impact factor, 146 H index, 4.17 Scopus impact factor).Our success depends completely on the voluntary investment of time and effort from you.We understand that there are many demands on your time and we appreciate the time you spend reading and commenting on manuscripts.Thank you for your willingness to serve in this role.Your expertise ensures that the papers published in this journal meet the high standards the research community expects.G-cubed received 485 manuscripts in 2023, publishing 244 of them.Our publications cover research, methods, and coding applications that span the gamut of the numerous AGU sections.Over 764 reviewers donated their expertise to the journal, providing over 937 reviews.We couldn't do this job without you.Reviewer names are listed below and names in italics are those who provided three or more reviews.We look forward to a 2024 of exciting advances in the field and communicating those advances to our community and the broader public.If you have comments regarding G-Cubed or its peer review process, we invite you to contact the journal at g-cubed@agu.org.
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 | 0.026 | 0.254 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.018 | 0.009 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.164 | 0.293 |
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".