Evolution of a Canada Basin ice‐ocean boundary layer and mixed layer across a developing thermodynamically forced marginal ice zone
Bibliographic record
Abstract
Abstract A comprehensive set of autonomous, ice‐ocean measurements were collected across the Canada Basin to study the summer evolution of the ice‐ocean boundary layer (IOBL) and ocean mixed layer (OML). Evaluation of local heat and freshwater balances and associated turbulent forcing reveals that melt ponds (MPs) strongly influence the summer IOBL‐OML evolution. Areal expansion of MPs in mid‐June start the upper ocean evolution resulting in significant increases to ocean absorbed radiative flux (19 W m−2 in this study). Buoyancy provided by MP drainage shoals and freshens the IOBL resulting in a 39 MJ m−2 increase in heat storage in just 19 days (52% of the summer total). Following MP drainage, a near‐surface fresh layer deepens through shear‐forced mixing to form the summer mixed layer (sML). In late summer, basal melt increases due to stronger turbulent mixing in the thin sML and the expansion of open water areas due in part to wind‐forced divergence of the sea ice. Thermal heterogeneities in the marginal ice zone (MIZ) upper ocean led to large ocean‐to‐ice heat fluxes (100–200 W m−2) and enhanced basal ice melt (3–6 cm d−1), well away from the ice edge. Calculation of the upper ocean heat budget shows that local radiative heat input accounted for at least 89% of the observed latent heat losses and heat storage (partitioned 0.77/0.23). These results suggest that the extensive area of deteriorating sea ice observed away from the ice edge during the 2014 season, termed the “thermodynamically forced MIZ,” was driven primarily by local shortwave radiative forcing.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| 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".