Palaeo-sea-level and palaeo-ice-sheet databases: problems, strategies, and perspectives
Bibliographic record
Abstract
Abstract. Sea-level and ice-sheet databases have driven numerous advances in understanding the Earth system. We describe the challenges and offer best strategies that can be adopted to build self-consistent and standardised databases of geological and geochemical information used to archive palaeo-sea-levels and palaeo-ice-sheets. There are three phases in the development of a database: (i) measurement, (ii) interpretation, and (iii) database creation. Measurement should include the objective description of the position and age of a sample, description of associated geological features, and quantification of uncertainties. Interpretation of the sample may have a subjective component, but it should always include uncertainties and alternative or contrasting interpretations, with any exclusion of existing interpretations requiring a full justification. During the creation of a database, an approach based on accessibility, transparency, trust, availability, continuity, completeness, and communication of content (ATTAC3) must be adopted. It is essential to consider the community that creates and benefits from a database. We conclude that funding agencies should not only consider the creation of original data in specific research-question-oriented projects, but also include the possibility of using part of the funding for IT-related and database creation tasks, which are essential to guarantee accessibility and maintenance of the collected data.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".