Assessing seasonal spatial segregation by age class of beluga whales (Delphinapterus leucas) in Western Hudson Bay estuaries
Bibliographic record
Abstract
Segregation of adult males from adult females and immature animals is known to occur in some beluga whale populations, but it is unclear if such segregation occurs in Hudson Bay, where the largest summering population in the world is found. Using imagery from a photographic aerial survey conducted in August 2015, we examined spatial distribution by age class with respect to several environmental variables near two of three main estuaries, Churchill and Seal River, used by Western Hudson Bay belugas in the summer season. Belugas photographed during aerial surveys were classified by age manually using an identification decision tree, and GPS coordinates of their locations were plotted in ArcGIS. Distribution by age class was examined in relation to five habitat characteristics: distance to coastal habitat, bathymetry, sea surface temperature, and extent of river plume. Habitat characteristics and the proportion of animals by age classes were similar in both estuaries, indicating no segregation, and suggesting the environmental data assessed were not associated with patterns of distribution and density of age classes at the spatial and temporal scale being investigated. Overall density of calves was almost three times higher at the Seal River; however, suggesting this location may be preferred for calf rearing in the summer season. Results provide a greater understanding of spatial patterns of beluga whale habitat use in western Hudson Bay, and information useful in conservation and management advice.
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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 | 0.000 | 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".