Modelling long term ice sheet changes to understand the stability of the Greenland Ice Sheet in a warmer world
Bibliographic record
Abstract
The Greenland Ice Sheet is under stress with the rising temperature. Ice sheet sensitivity to temperature changes and its contribution to global sea level rise can be estimated using ice sheet/climate models. This study combines an ice dynamics model and an isotope tracer model to carry out long-term coupled ice-sheet/climate simulations to investigate the vulnerability of the Greenland ice sheet to higher temperatures during the Eemian warming. I also examine how Eemian melting may have altered isotopic ratios and temperature reconstructions. The simulations are benchmarked against NEEM and Summit ice core observations to provide validation of model performance. I find that there is ice in central Greenland for climates up to 12ºC higher than present. Greenland’s contribution due to Eemian sea level most likely ranges between 2.8 to 4.3 m, associated with temperature anomalies from 5 to 9ºC. Melt-induced isotopic modifications can cause overestimation of 0.1-2ºC warming in proxy records.
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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".