An Update on the Ice Climatology of the Hudson Bay System
Bibliographic record
Abstract
The objective of this paper is to examine the thermodynamic and dynamic forcing of sea ice within the Hudson Bay System, including Hudson Bay, Hudson Strait, and Foxe Basin. Changes in fall and spring sea ice extents (SIEs) are examined in relation to seasonal surface air temperatures (SATs) and winds, as are changes in freeze-up dates and breakup dates. The proportional leverage of the fall (lag1) and spring SATs and winds on ice is statistically examined per basin. Results show SATs have increased significantly since the mid-1990s and that increases in the fall are higher than the spring period. Fall SATs are highly related to fall SIEs (R 2 = 0.79–0.82). For every 1 °C increase in SAT, SIE decreases by 14% (% of basin area) within the Hudson Bay System; a 1 °C increase delays freezeup by 0.7 to 0.9 weeks on average. Spring SIEs and breakup dates are shown to be highly correlated with fall (lag1) and spring SATs, and with U and V component winds. Proportionately, spring and fall SATs combined play a dominant role (70–80%) in SIE, and the remaining leverage is attributed to dynamic forcing (winds). The relative leverage of fall (lag1) SATs and surface winds are shown to be significant and vary by basin. The open water season has on average increased by 3.1 (±0.6) weeks in Hudson Bay, 4.9 (±0.8) weeks in Hudson Strait, and 3.5 (±0.9) weeks in Foxe Basin.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".