Palaeo sea-level and ice-sheet databases: problems, strategies and perspectives
Bibliographic record
Abstract
Abstract. Sea-level and ice-sheet databases are essential tools for evaluating palaeoclimatic changes. However, database creation poses considerable challenges and problems related to the composition and needs of scientific communities creating raw data, the compiliation of the database, and finally using it. There are also issues with data standardisation and database infrastructure, which should make the database easy to understand and use with different layers of complexity. Other challenges are correctly assigning credit to original authors, and creation of databases that are centralised and maintained in long-term digital archives. Here, we build on the experience of the PALeo constraints on SEA level rise (PALSEA) community by outlining strategies for designing a self-consistent and standardised database of changes in sea level and ice sheets, identifying key points that need attention when undertaking the task of database creation.
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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 | 0.090 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.030 | 0.038 |
| Open science | 0.008 | 0.009 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".