Variability of water properties, currents and fluxes in Nares Strait, connecting the Arctic to the Atlantic Ocean
Bibliographic record
Abstract
No abstracts are to be cited without prior reference to the author. Enhanced delivery of fresh cold waters from the Arctic along Labrador contributes to vertical stratification as far south as the Mid-Atlantic Bight where interannual ecosystem variability appears to correlate with upstream conditions in the Canadian Arctic Archipelago (CAA). Nares Strait between Greenland and the CAA provides a seawater pathway between the Arctic and Atlantic Oceans. A mooring array across the strait has provided information on water properties, currents and fluxes from 2003 to 2012. These are strongly influenced by the sea ice state in the strait, which varies from mobile in summer to land-fast in winter caused by ice bridge formation to the south. The geostrophic freshwater flux shows fluctuations linked to change in weather and season (enhanced during mobile ice season). Change in the annual ice cover cycle, such as recent failures of ice bridge formation, greatly extends the mobile ice season. A future increase of failures could allow a higher freshwater flux to the south, deeper mixing of brackish surface waters, and a stronger dependence of flow on local atmospheric forcing. Our developing understanding of fluxes through Nares Strait will guide future improvement in the representation of the global hydrologic cycle in 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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".