On the robustness of predictions of sea level fingerprints
Bibliographic record
Abstract
The rapid melting of the Earth′s ice reservoirs will produce geographically distinct patterns of sea level change that have come to be known as sea level fingerprints. A basic, gravitationally self-consistent theory for computing these patterns appeared in the 1970s; however, recent, highly discrepant fingerprint calculations have led to suggestions that the algorithms and/or theoretical implementation adopted in many previous predictions is not robust. We present a suite of numerical predictions, including benchmark comparisons with analytic results, that counter this argument and demonstrate the accuracy of most published predictions. Moreover, we show that small differences apparent in calculations published by some groups can be accounted for by subtle differences in the underlying physics. The paper concludes with two sensitivity analyses: (1) we present the first-ever calculation of sea level fingerprints on earth models with 3-D variations in elastic structure and density, and conclude that this added complexity has a negligible effect on the predictions; (2) we compare fingerprints of polar ice sheet mass flux computed under the (very common) assumption of a uniform melt distribution to fingerprints calculated using melt geometries constrained by analysing recent trends in GRACE gravity data. Predictions in the near field of the ice sheets are sensitive to the assumed melt geometry; however, this sensitivity also extends to the far field, particularly in the case of Antarctic mass changes, because of the strong dependence of the rotational feedback signal on the melt geometry. We conclude that inferences of ice sheet mass flux based on modern sea level constraints should consider these more realistic melt geometries.
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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 | 0.009 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".