Role of climatological and geophysical controls on the landfast sea ice regime in the Hudson Bay region
Bibliographic record
Abstract
This study investigates how both climatological and geophysical factors impact and have changed the landfast sea ice (LFI) regime in Hudson Bay and James Bay (HJB), Canada. LFI plays an important role in coastal land use practices, traditional livelihood and the formation of wetland ecosystems. Stability and extent of LFI platform is crucial to coastal communities as they use it for travel, fishing and hunting. The most vital concern raised in the Hudson Bay Summit 2022, was regarding the unpredictability of the LFI in terms of presence and thickness, which endangers the ecosystem services and livelihood of these coastal communities. The investigation relied on three sub-objectives: 1) trends of fast ice persistence and extent across HJB from 2001-2018; 2) impact of climatological factors on the landfast ice cycle, and 3) how coastal topography impacts the fast ice cycle. For this study we utilised an array of remote sensing and reanalysis products to study variables such as the landfast ice cycle (freeze-up, break-up) and persistence (CIS Ice charts, MODIS), air temperature (ERA5 reanalysis product), snow melt on land (MOD10A2 snow cover product), coastline orientation (Landsat) and coastal bathymetry (GEBCO). In addition to notable east-west contrast of the LFI climatology in HJB, the observations reveal how coastal topography impacts ice stability and extent, and eventually influences ice persistence, and how a positive feedback is created between the LFI and local air temperature. An understanding of these interlinkages are of critical importance to improve the prediction of LFI breakup in face of rapid climate warming and increased variability. The trends revealed through this study were unique compared to other Sub-Arctic regions with seasonal ice cover. Hence, a focused investigation of the factors that works as precursors of ice freeze-up and triggers break-up is proven to be vital to the continued safe use of the LFI platform.
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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 | 0.002 |
| 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 | 0.001 |
| 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".