Environmental factors and behaviour of St. Lawrence Estuary Beluga generate heterogeneity in availability bias for photographic and visual aerial surveys
Bibliographic record
Abstract
In absence of adequate data, abundance estimates for St. Lawrence Estuary (SLE) beluga obtained from visual surveys have been corrected for availability bias using factors developed for photographic surveys. Not accounting for the longer detection time associated with visual surveys will lead to an overestimation of beluga abundance relative to indices obtained from photographic surveys. This study offers a comprehensive analysis of the relative influence of multiple methodological, environmental and behavioural factors on availability bias estimates for both photographic and visual surveys using detailed dive profiles from 27 SLE beluga. As expected, availability estimates were systematically higher for visual surveys than for photographic surveys for which time-in-view is instantaneous. However, for photographic surveys the change in methodology for estimating availability from an approach based on group visibility to one where the detailed diving patterns of individuals were logged, led to a 26—42% decrease in mean availability estimates. Our results confirmed that dives are longer when animals are inside compared to outside areas of high density (AHD), consistent with the prediction that these areas are used for behaviour like foraging. They also indicate that while some of the behavioural or environmental factors such as latent processes associated with the zone used may have a notable effect on availability, survey design (photographic or visual), characteristics of survey platforms, and observer searching patterns may be the most influential factors on availability bias. We conclude that previous estimates of SLE beluga abundance from photographic surveys were likely biased downward by an overestimation of beluga availability, and by not considering the uneven distribution of beluga among different zones with specific but undefined underlying processes.
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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 | 0.002 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".