Landfast Sea Ice Phenology in Hudson Bay and James Bay: Seasonal Trends, Spatial Patterns and Local Processes
Bibliographic record
Abstract
Landfast sea ice, essential to the ecosystems and communities in Sub-Arctic Canada, supports marine life, shapes coastal oceanographic regimes, and facilitates transportation and hunting for Indigenous communities. Understanding its dynamics is critical for developing hazard mitigation and adaptation strategies as climate change accelerates. This thesis examines landfast ice cover in Hudson Bay and James Bay, focusing on meteorological, oceanographic, and geomorphological factors affecting its growth, stability, and decay. The study examines how landfast ice is influenced by both direct factors, such as air temperature, and indirect factors, like snowmelt timing, which shows a stronger correlation with ice break-up dates than spring temperatures alone. Additional variables, such as river discharge and coastal topography, further impact ice persistence and stability. Areas near river outflows are investigated for causes of later ice formation and earlier melt, in contrast to coastlines with specific orientations and underwater topographies that support longer-lasting ice by shielding it from wind, waves, and storms. Analysis over the past two decades shows a decreasing duration of landfast ice in the northern and southwestern Hudson Bay, as well as parts of western and southern James Bay, while eastern Hudson Bay and James Bay exhibit stable or increasing trends. This research provides a comprehensive baseline on the spatio-temporal variability of landfast ice and the factors influencing its regime, supporting future monitoring, modeling, and policy-making for Hudson Bay and James 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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".