On the inter-connectivity of volume transports through Arctic Straits
Bibliographic record
Abstract
Arctic heat and freshwater budgets are highly sensitive to volume transports through Arctic-Subarctic straits. Here we investigate how the volume transports through these straits adjust to each other to maintain a mass balance in the Arctic on annual timescales. To this end, we use three models; two coupled global climate models, one with a third-degree horizontal ocean resolution (HiGEM1.1) and one with a twelfth-degree horizontal ocean resolution (HadGEM3), and one ocean-only model with an idealized polar basin (tenth-degree horizontal resolution). The two global climate models indicate that there is a strong anti-correlation between the Bering Strait throughflow and the transport through the Nordic Seas, a second strong anti-correlation between the transport through the Canadian Artic Archipelago (CAA) and the Nordic Seas transport, and a third strong anti-correlation between the Fram Strait and the Barents Sea throughflows. We find that part of the strait correlations is due to the strait transports being coincidentally driven by large-scale atmospheric forcing patterns such as the Arctic Oscillation. However, there is also a role for fast wave adjustments of some straits flows to perturbations in other straits since atmospheric forcing of individual strait flows alone cannot lead to near mass balance fortuitously every year. Idealized experiments with an ocean model (NEMO3.6) that investigate such causal strait relations suggest that perturbations in the Bering Strait are compensated preferentially in the Fram Strait due to the narrowness of the western Arctic shelf and the deeper depth of the Fram Strait.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".