Iceberg Calving: Regimes and Transitions
Bibliographic record
Abstract
Uncertainty about sea-level rise is dominated by uncertainty about iceberg calving, mass loss from glaciers or ice sheets by fracturing. Review of the rapidly growing calving literature leads to a few overarching hypotheses. Almost all calving occurs near or just downglacier of a location where ice flows into an environment more favorable for calving, so the calving rate is controlled primarily by flow to the ice margin rather than by fracturing. Calving can be classified into five regimes, which tend to be persistent, predictable, and insensitive to small perturbations in flow velocity, ice characteristics, or environmental forcing; these regimes can be studied instrumentally. Sufficiently large perturbations may cause sometimes-rapid transitions between regimes or between calving and noncalving behavior, during which fracturing may control the rate of calving. Regime transitions underlie the largest uncertainties in sea-level rise projections, but with few, important exceptions, have not been observed instrumentally. This is especially true of the most important regime transitions for sea-level rise. Process-based models informed by studies of ongoing calving, and assimilation of deep-time paleoclimatic data, may help reduce uncertainties about regime transitions. Failure to include calving accurately in predictive models could lead to large underestimates of warming-induced sea-level rise. ▪Iceberg calving, the breakage of ice from glaciers and ice sheets, affects sea level and many other environmental issues.▪Modern rates of iceberg calving usually are controlled by the rate of ice flow past restraining points, not by the brittle calving processes.▪Calving can be classified into five regimes, which are persistent, predictable, and insensitive to small perturbations.▪Transitions between calving regimes are especially important, and with warming might cause faster sea-level rise than generally projected.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".