Sea ice climatology in the Canadian Western Arctic: thermodynamic<i>versus</i>dynamic controls
Bibliographic record
Abstract
ABSTRACT Based on the regions in the western Canadian Arctic as outlined by the Canadian Ice Service, the normals and trends from 1981 to 2010 were analysed for the monthly surface air temperature, monthly wind speed and direction. For the month of September, the temperatures from 1981 to 2010, for all of the defined regions, increased by 2–4 °C. The monthly concentrations of sea ice and multiyear ice were analysed for normals and trends from 1981 to 2010. Although the whole Arctic has seen a large reduction in the minimum sea ice extent, in the defined region of study during September, only the Beaufort Sea region shows a statistically significant decrease in sea ice concentrations. Correlations between the climatological state variables and sea ice were investigated in this study to determine relative thermodynamic and dynamic contributions to a decline in sea ice extent in the Western Arctic. As expected, the regions of interest all showed a statistically significant correlation between the surface air temperatures and the total sea ice concentrations. However, neither the wind speed nor the direction had a strong correlation on the sea ice concentration trends. This study showed that in the regions investigated, thermodynamic forcing of sea ice is the dominant driver when compared with dynamic forcing, which is a secondary driver in the western Canadian Arctic.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".