Antarctic Ice Sheet paleo-constraint database
Bibliographic record
Abstract
Abstract. We present a database of observational constraints on past Antarctic Ice Sheet changes during the last glacial cycle intended to consolidate the observations that represent our understanding of past Antarctic changes and for state-space estimation and paleo-model calibrations. The database is a major expansion of the initial work of Briggs and Tarasov (2013). It includes new data types and multi-tier data quality assessment. The updated constraint database, AntICE2 (https://theghub.org/resources/4884, Lecavalier et al., 2022), consists of observations of past grounded- and floating-ice-sheet extent, past ice thickness, past relative sea level, borehole temperature profiles, and present-day bedrock displacement rates. In addition to paleo-observations, the present-day ice sheet geometry and surface ice velocities are incorporated to constrain the present-day ice sheet configuration. The method by which the data are curated using explicitly defined criteria is detailed. Moreover, the observational uncertainties are specified. The methodology by which the constraint database can be applied to evaluate a given ice sheet reconstruction is discussed. The implementation of the AntICE2 database for Antarctic Ice Sheet model calibrations will improve Antarctic Ice Sheet predictions during past warm and cold periods and yield more robust paleo-model spin ups for forecasting future ice sheet changes.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.019 |
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".