The effect of structural porosity on the ablation of sea ice ridges
Bibliographic record
Abstract
Observations reveal that the decrease in ice thickness through melting in summer is much more rapid for ridges than for surrounding level ice. A physical model that represents internal melting within ridge keels has been developed to explain this observed draft‐dependent ablation for first‐year pack ice in the Beaufort Sea. The porous structure of a ridge keel permits percolation of a substantial fraction of the oncoming oceanic flow, up to 20% for a feature with 30% porosity and 9‐m draft. The percolating flow delivers oceanic heat to a large surface area deep within the keel and increases melt rates relative to surrounding level ice by a factor of 5 when seawater temperatures are 0.18 degrees above freezing. Melt rates are sensitive to the internal geometry of ridges through keel porosity and block dimensions, characteristics that vary widely between ridge features. However, the average rate of melting as a function of draft, calculated for a realistic population of keels with average cross‐sectional shape and differing draft, has the same draft‐dependence as the observations. This concurrence suggests that the process of internal melting may be dominant in the ablation of ridged ice. In addition, internal melting during the summer may well hasten structural consolidation of surviving ridge keels through freezing during the following winter. It appears that the evolution of the thickest ice within the Arctic ice pack is dependent on the small‐scale structural characteristics of the ridged ice and its interaction with the upper layer of the ocean.
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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 | 0.000 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".