A Comparison of Arctic Ocean Sea Ice Export Between Nares Strait and the Canadian Arctic Archipelago
Bibliographic record
Abstract
Abstract Nares Strait and the channels of the Canadian Arctic Archipelago (CAA) act as conduits for sea ice export from the Arctic Ocean but have never been directly compared. Here, we perform such a comparison for both the sea ice area and volume fluxes from October 2016 to December 2021. Nares Strait provided the largest average seasonal (October through September) ice area flux of 95 ± 8 × 103 km2 followed by the CAA regions of the Queen Elizabeth Islands (QEI) at 41 ± 7 × 103 km2 and M’Clure Strait at 2 ± 8 × 103 km2 with corresponding ice volume fluxes of 177 ± 15 km3, 59 ± 10 km3, and 8 ± 8 km3, respectively. Larger Arctic Ocean ice export at Nares Strait was associated with a shorter ice arch duration (237 days) compared to M’Clure Strait (163 days) and QEI (65 days). Seasonal Arctic Ocean ice export was dominated by Nares Strait in 2017–2019 and 2021 but was remarkably exceeded by the QEI in 2020. Large‐scale atmospheric circulation patterns were found to influence the ice area flux in the absence of ice arches but no occurrence of coherent Arctic Ocean ice export events coinciding across all gates were observed. Average net seasonal Arctic Ocean ice area and volume export were 138 × 103 km2 and 245 km3, which represent ∼16% of the area and ∼25% of the volume of sea ice export from Fram Strait. Divergent Arctic Ocean export ice trajectories are apparent for Nares Strait and the QEI when compared to 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".