The effect of floe size on the flow of ice covers through converging channels
Bibliographic record
Abstract
The present report describes work done to examine the effect of floe size on the drift of ice covers. Flow through a converging channel is used as a test case. The testing examined the drift of continuous ice covers as well as ice covers consisting of assemblies of distinct deformable floes. The results showed that the drift of all ice cover types through converging channels can be represented by a relationship between two nondimensional variables. The first variable is a non-dimensional drift velocity (normalized by the free drift velocity). The second variable represents the ratio between the environmental driving force (e.g. wind drag) and the strength of the ice cover. The results showed that the existence of distinct floes in the ice cover slows the drift through constrictions. The decrease of the drift velocity becomes more pronounced with increasing floe sizes. Moreover, the effects of floe size also become apparent for relatively small environmental driving forces. For higher forces (e.g. higher wind velocities), even large floes are pushed through the constrictions. The present approach proved to be capable of quantifying the influence of floe size on the drift. The present work can thus be extended to serve as a basis for developing forecast products to guide the prediction of the drift of ice covers containing relatively large floes. Such products will require examining a wider range of geometries, floe sizes and ice properties. The report suggests that analysis of available records can be used to provide forecast plots for specific locations of interest.
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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 | 0.001 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".