Increased Arctic Ocean sea ice loss through the Canadian Arctic Archipelago under a warmer climate
Bibliographic record
Abstract
The ice arches that ring the northern Canadian Arctic Archipelago have historically blocked the inflow of Arctic Ocean sea ice for the majority of the year. However, annual average air temperature in northern Canada has increased by more than 2°C over the past 65+ years and a warmer climate is expected to contribute to the deterioration of these ice arches, which in turn has implications for the overall loss of Arctic Ocean sea ice. We investigated the effect of warming on the Arctic Ocean ice area flux into the Canadian Arctic Archipelago using a 22-year record (1997-2018) of ice exchange derived from RADARSAT-1 and RADARSAT-2 imagery. Results indicated that there has been a significant increase in the amount of Arctic Ocean sea ice (103 km2/year) entering the northern Canadian Arctic Archipelago over the period of 1997-2018. The increased Arctic Ocean ice area flux was associated with reduced ice arch duration but also with faster (thinner) moving ice and more southern latitude open water leeway as a result of the Canadian Arctic Archipelago’s long-term transition to a younger and thinner ice regime. Remarkably, in 2016, the Arctic Ocean ice area flux into the Canadian Arctic Archipelago (161x103 km2) was 7 times greater than the 1997-2018 average (23x103 km2) and almost double the 2007 ice area flux into Nares Strait (87x103 km2). Indeed, Nares Strait is known to be an important pathway for Arctic Ocean ice loss however, the results of this study suggest that with continued warming, the Canadian Arctic Archipelago may also become a large contributor to Arctic Ocean ice loss.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".