Thank You to Our 2021 Reviewers
Bibliographic record
Abstract
Abstract On behalf of the Editorial Board and Staff of Earth and Space Science, I thank the reviewers whose selfless dedication to science has ensured, once again, that the papers published in our journal in 2021 highlight the best Earth and space science in a manner that does justice to the authors and their work. All of us at Earth Peer reviewing is a demanding and often thankless job. It is however an essential component of the scientific process, ensuring the highest standards of integrity and rigor. Without the work of reviewers, who check data and procedures for possible bias and to ensure reproducibility, and who share their expertise to verify that the interpretations and conclusions of a paper are consistent with assumptions and existing knowledge, it would not be possible to trust in the scientific process. Our journal is particularly indebted to our reviewers: Earth and Space Science is a multidisciplinary journal that highlights methods, instruments, data and algorithms, and therefore we rely heavily on the direct expertise of our reviewers to verify and vouch for the quality of the papers we publish. We are indebted to all our reviewers, and we are delighted to acknowledge them publicly in this Editorial.
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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 | 0.030 | 0.302 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.020 | 0.008 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.054 | 0.093 |
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".