Studies of the Canadian Arctic Archipelago water transport and its relationship to basin‐local forcings: Results from AO‐FVCOM
Bibliographic record
Abstract
Abstract A high‐resolution (up to 2 km), unstructured‐grid, fully coupled Arctic sea ice‐ocean Finite‐Volume Community Ocean Model (AO‐FVCOM) was employed to simulate the flow and transport through the Canadian Arctic Archipelago (CAA) over the period 1978–2013. The model‐simulated CAA outflow flux was in reasonable agreement with the flux estimated based on measurements across Davis Strait, Nares Strait, Lancaster Sound, and Jones Sounds. The model was capable of reproducing the observed interannual variability in Davis Strait and Lancaster Sound. The simulated CAA outflow transport was highly correlated with the along‐strait and cross‐strait sea surface height (SSH) difference. Compared with the wind forcing, the sea level pressure (SLP) played a dominant role in establishing the SSH difference and the correlation of the CAA outflow with the cross‐strait SSH difference can be explained by a simple geostrophic balance. The change in the simulated CAA outflow transport through Davis Strait showed a negative correlation with the net flux through Fram Strait. This correlation was related to the variation of the spatial distribution and intensity of the slope current over the Beaufort Sea and Greenland shelves. The different basin‐scale surface forcings can increase the model uncertainty in the CAA outflow flux up to 15%. The daily adjustment of the model elevation to the satellite‐derived SSH in the North Atlantic region outside Fram Strait could produce a larger North Atlantic inflow through west Svalbard and weaken the outflow from the Arctic Ocean through east Greenland.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".