High‐frequency aerial surveys inform the seasonal distribution of Cook Inlet beluga whales
Bibliographic record
Abstract
ABSTRACT Management options to mitigate potential effects from the overlap of anthropogenic and marine mammal activities require understanding of species’ habitat use and movement patterns. We analyzed high temporal frequency industrial marine mammal monitoring program aerial surveys conducted over a protracted period from April to October of 2013 and 2014 to examine beluga whale ( Delphinapterus leucas ) ecology in the Cook Inlet, Alaska, USA. Our objectives were to characterize the spatiotemporal whale distributions, determine the utility of these patterns to inform management practices, and assess industrial survey data as a supplement to agency population monitoring programs. Cook Inlet beluga whale densities peaked in late June to early July, with some activity observed in all survey months. Consistent trends in spatial persistence were identified and found to be associated with the seasonal sequence of prey field distributions including eulachon ( Thaleichthys pacificus ), Pacific salmon ( Oncorhynchus spp.), and gadids ( Gadidae spp.). Seasonal beluga whale distributions were stable across both study years, indicating hotspots of habitat use with predictable seasonal timing. The regular temporal and spatial distribution of beluga whale activity suggests potential to inform management efforts to mitigate disturbance of whales from anthropogenic activities in the Inlet. We found high‐frequency surveys conducted as part of industrial marine mammal monitoring programs provided a useful additional data source for population monitoring; however, improvements in survey design and efforts to control for observer detection biases may further increase the utility of these surveys to complement ongoing standardized scientific surveys. © 2018 The Wildlife Society.
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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 | 0.000 |
| 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".