Heat loss from the Atlantic water layer in the northern Kara Sea: causes and consequences
Bibliographic record
Abstract
Abstract. A distinct, subsurface density front along the eastern St. Anna Trough in the northern Kara Sea is inferred from hydrographic observations in 1996 and 2008–2010. Direct velocity measurements show a persistent northward subsurface current (~ 18 cm s−1) along the St. Anna Trough eastern flank. This sheared flow, carrying the outflow from the Barents and Kara seas to the Arctic Ocean, is also evident from shipboard observations as well as from geostrophic velocities and numerical model simulations. Although we cannot substantiate our conclusions by direct observation-based estimates of mixing rates in the area, we hypothesize that the enhanced vertical mixing along the St. Anna Trough eastern flank favors the upward heat loss from the intermediate warm Atlantic water layer. Modeling results support this hypothesis. The upward heat flux inferred from hydrographic data and model simulations is of O(30–100) W m−2. The region of lowered sea ice thickness and concentration seen both in sea ice remote sensing observations and model simulations marks the Atlantic water pathway in the St. Anna Trough and adjacent Nansen Basin continental margin. In fact, the sea ice shows a delayed freeze-up onset during fall and a reduction in the sea ice thickness during winter. This is consistent with our results on the enhanced Atlantic water heat loss along the Atlantic water pathway in the St. Anna Trough.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".