Bibliographic record
Abstract
Abstract. We here describe, document, and make available a wide range of data sets used for annual layer identification in ice cores from DYE-3, GRIP, NGRIP, NEEM, and EGRIP. The data stem from detailed measurements performed both on the main deep cores and shallow cores over more than forty years using many different setups developed by research groups in several countries, and comprise both discrete measurements from cut ice samples and continuous-flow analysis data. The data series were used for the construction of the Greenland Ice-Core Chronology 2005 (GICC05) and/or the revised GICC21. Now that the underlying data are made available, we also release the individual annual layer positions of the GICC05 time scale which are based on these data sets. We hope that the release of the data sets will stimulate further studies of the past climate taking advantage of these highly resolved data series covering a large part of the interior of the Greenland ice sheet.
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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 | 0.003 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.018 | 0.013 |
| Insufficient payload (model declined to judge) | 0.193 | 0.149 |
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".