Modeling the seasonal variation of sea ice in the Labrador Sea with a coupled multicategory ice model and the Princeton ocean model
Bibliographic record
Abstract
We use a multicategory sea ice model coupled to the Princeton ocean model, which is driven by monthly climatological atmospheric forcing, to study the seasonal variation of ice cover in the Labrador Sea. Initial ocean conditions are derived from a gridded, objectively analyzed temperature‐salinity data set that provides improved resolution of gradients in the vicinity of the shelf break. The model produces a realistic seasonal variation of sea ice. There is ice growth over the inner shelf and ice melt over the outer shelf and slope. Over the inner shelf, advection and diffusion decrease the ice mass; over the outer shelf, advection and diffusion increase the ice mass, which maintains the location of the ice edge. Near the offshore ice edge the melt rate exceeds 1 m per month, and the heat to melt ice together with the heat lost to the atmosphere exceeds 500 W m−2. The heat lost at the ocean surface is compensated for by advection of heat from an offshore convective region. The dominant heat source for the spring retreat of ice in the south is shortwave radiation over the open water fraction.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".