Availability bias adjustment for calculating aerial survey abundance estimates for Belugas (Delphinapterus leucas) in the Eastern Beaufort Sea
Bibliographic record
Abstract
Abundance estimates of whales from aerial surveys need to be adjusted for animals that are underwater during the survey and cannot be counted by survey observers. In 2019, an aerial survey was conducted to estimate the abundance of the Eastern Beaufort Sea beluga (Delphinapterus leucas) population. To complement the survey, belugas were equipped with satellite transmitters to record their time at depth and determine adjustment factors for the survey estimate. Two types of adjustments factors, instantaneous and non-instantaneous, were computed depending on the type of survey (photographic vs. with visual observers) and the area surveyed (inshore vs. offshore). The instantaneous adjustment factor was calculated for use in the photographic strata of the survey of the inshore areas based on the proportion of time belugas spent within 1 m of the surface. The non-instantaneous adjustment factor was computed for the observer-based strata of the survey in the offshore areas. The non-instantaneous adjustment factor was based on the proportion of time belugas spent within 5 m of the surface and was computed using the Laake method. The resulting adjustment factors were 1.56 (S.D. = 0.592) for the instantaneous and 1.94 (S.D. = 0.521) for the non-instantaneous.
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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 | 0.012 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".