Modelling beluga habitat use and baseline exposure to shipping traffic to design effective protection against prospective industrialization in the Canadian Arctic
Bibliographic record
Abstract
Abstract Global warming is predicted to reduce sea ice and thereby grant access to new shipping routes in the Arctic, leading to the expansion of human exploitation of natural resources in this region. The accompanying rise in boat numbers could impact the local populations of marine mammals by increasing collision rates and behavioural disturbance. It is therefore important to quantify the baseline exposure to current levels of shipping traffic and to understand how wildlife's important habitat overlaps with shipping lanes, in order to support appropriate spatial planning and management. In this study, telemetry tracks from nine belugas ( Delphinapterus leucas ) tagged in Western Hudson Bay, which is home to the world's largest summer aggregation of this species, were used to estimate the habitat use of the animals and to map any overlap with current shipping activities. Following a use–availability design, with spatially adaptive smooths fitted using generalized estimating equations, beluga habitat use was quantified, confirming that they aggregate in coastal areas in association with river estuaries. The baseline exposure is low, and is concentrated around major harbours in the region. Rising levels of traffic will increase anthropogenic pressure on Western Hudson Bay belugas. The approach presented here informs the design of effective spatial protection measures to minimize any potential consequence on the population.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".