Investigating Harvest and Vessel Traffic Exposure as Drivers of Social Group Characteristics in Canadian Beluga ( <scp> <i>Delphinapterus leucas</i> </scp> ) Populations
Bibliographic record
Abstract
ABSTRACT Assessing variation in social behaviors of group‐living animals may provide insight into the impacts of population stressors. Using very high resolution (VHR) satellite imagery acquired over 3 years, we compared social group size and composition, as well as spatial and social cohesion in three beluga ( Delphinapterus leucas ) populations that experience different levels of harvest and vessel traffic exposure: Cumberland Sound, Eastern High Arctic‐Baffin Bay, and Western Hudson Bay. We further explored the relationships between harvest, vessel activity, beluga density, and social context with beluga grouping characteristics to predict potential drivers of beluga social group dynamics. Mean group size decreased with harvest levels, possibly reflecting the effects of population decline or removal of key social individuals. Populations exposed to recent increases in vessel activity were associated with larger group sizes and greater spatial cohesion, possibly suggesting increased vigilance in response to vessel traffic. Adult‐juvenile mixed groups were larger and had smaller inter‐group distances than groups comprised of adults only, likely reflecting the dependence of younger whales on alloparental care. This study provides a novel application of VHR satellite imagery for beluga monitoring and highlights potential impacts of anthropogenic stressors on beluga populations.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".