Geometrical constraints on the evolution of ridged sea ice
Bibliographic record
Abstract
A numerical model of the evolving draft distribution of seasonal pack ice is driven by freezing and ice field compression in one dimension. Spatial transects of sea ice draft acquired during winter in the Beaufort Sea are used to evaluate the model. Histograms obtained by ice‐profiling sonar on subsea moorings reveal changes in the draft distribution, while observations of ice velocity by Doppler sonar allow calculation of the strain to which the draft distribution is responding. Numerical diffusion in thermal ice growth is controlled using a remapping algorithm. Mechanical redistribution algorithms in common use generate much more deep ridged ice than is observed. Geometric constraints on ridge‐keel development that reflect the finite extent of level floes available for ridge building and the true average shape of keels produce more realistic results. In the seasonal pack ice of the Beaufort Sea, 75% of all floes are too small to provide a volume of ice sufficient to construct a keel of draft equal to that commonly assumed in ice dynamics modeling. On average, the distribution of draft within keels has a negative exponential form, implying a cusped keel shape with more area on the thinner flanks than at the crest; models commonly assume a uniform redistribution of ice into a keel of triangular shape. Clearly, the spatial organization of ice within seasonal pack or, equivalently, the existence of ridges and floes should be an acknowledged factor in redistribution theory for pack ice thickness.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".