Variability and Predictability of the Bering Strait Ocean Heat Transport and Arctic Ocean Sea Ice Extent
Bibliographic record
Abstract
Background: This study examines the monthly, seasonal, and interannual variations in Pacific Ocean heat transport entering the Arctic Ocean through the Bering Strait, and its influence on sea ice extent in the Arctic Ocean. Methods: Monthly ocean heat transport is calculated using temperature and volumetric transport data from moorings deployed in the Bering Strait. Pearson correlations are calculated between the observed detrended monthly cumulative Bering Strait ocean heat transport and the detrended monthly sea ice extent time series from May through September. Results: An increase in the spring variability of the Bering Strait ocean heat transport is found since 2010, associated with both increased volume flux and water temperatures in May and June. A significant negative correlation between the Bering Strait ocean heat transport and Arctic sea ice extent in the Pacific sector is observed for May, June, and July, both within and outside the marginal ice zone, with a sharp decline in predictability for August and September. Conclusion: The Bering Strait ocean heat transport is a skillful predictor for early melt season sea ice extent in the Pacific sector but loses predictive skills later in the summer in August and September due to changes in ice dynamics, in accordance with the loss of predictive skill in Global Climate Models.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".