Statistical Approaches for Modeling Ice Sheet Interconnectivity
Bibliographic record
Abstract
The greatest sources of uncertainty for future sea-level rise are the Greenland and Antarctic ice sheets. An important aspect of this uncertainty is the potential interconnectivity between them, which may amplify underlying instabilities in individual ice sheets. We explore these connections empirically by modelling the ice sheets as a cointegrated system. We consider two specications which allow the ice sheets to follow either an I(1) or an I(2) process in order to disentangle the long-run theory consistent relationships in the data. We examine the stability of these relationships over time both in and out of sample and eximine how a sudden loss of ice in Greenland propagates through the system. We show that a 1 Gigatonne loss of ice leads to a large and persistent loss of ice in West Arctica which is partially offset by an accumulation of ice in East Antarctica. Accounting for the long-run interactions between the ice sheets helps to improve our understanding of future instabilities and provides useful projections of the future paths of the ice sheets.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".