Interannual Variability of Hudson Bay Ice Thickness
Bibliographic record
Abstract
Seasonal sea ice in Hudson Bay plays a key role in determining the regional climatology. In this paper, the relationship between ice thickness with local surface air temperature and snow depth is explored at nine locations in the Hudson Bay region. A weak but statistically significant correlation was found between basin averaged ice thickness and concurrent surface air temperature. At the local scale, however, ice thickness correlated well with winter air temperature at only three measuring sites, explaining the poor relationship at the basin scale. A relationship was also identified between winter ice thickness and previous summer's air temperatures at two measuring sites, suggesting that preconditioning of Hudson Bay waters may play a significant role in sea-ice formation in some subregions of Hudson Bay. Simple and multiple linear regression analyses indicate that at the majority of the measuring sites, snow depth is a more important contributor to the inter-annual variability of ice thickness than winter air temperatures. The results of this study have important implications regarding the use of landfast ice thickness data to detect an early climate change signal over Hudson Bay.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".