Recent trends and scenarios of climate change in Hudson Bay and surrounding seas
Bibliographic record
Abstract
This study compares the response of Hudson Bay to a transient warming scenario provided by six coupled atmosphere-ocean general circulation models. The analysis focused on surface temperature, precipitation, sea-ice coverage, and permafrost distribution. The response of the sea-ice cover and permafrost to climate change was found to vary considerably among the models and thus large differences were observed in the projected regional increase in temperature and precipitation. In addition, the climate and hydrology of the Hudson Bay region were analysed for trends in order to provide early evidence of climate change in the region. The Theil-Sen slope approach was used to identify the magnitude of the trends and the Mann-Kendall test assessed their statistical significance. The results showed that warming over Hudson Bay has not occurred in a unidirectional way and, consequently, the trends are sensitive to the time periods chosen for analysis. The warming trends of the last few decades are reflected in the timing of sea-ice formation and break-up, but the strong dependence of ice thickness on snow depth suggests that it is more challenging to identify an early climate change signal using ice thickness data than it is using freeze-up/break-up dates. An asymmetry was observed in the trends of maximum ice thickness, as the ice cover has become thicker over time on the western side of Hudson Bay, while slightly thinning trends and statistically significant trends towards an earlier occurrence of the peak ice thickness were detected on the eastern side. It is inevitable that increasing temperatures in the Hudson Bay region will result in a thinning of the ice cover in the long run, but changes in snow depth will tend to offset the ice thickness trends in the short run. If this trend towards a longer ice-free season continues, as the models suggest, it will have disastrous consequences for the polar bear populations that inhabit the region.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".