EBS beluga population abundance estimate
Bibliographic record
Abstract
The Eastern Beaufort Sea (EBS) beluga population abundance was last assessed in July 1992. However, there is new evidence indicating that the EBS beluga distribution extends beyond the area covered by the 1992 survey. Visual and photographic surveys were conducted from July 21 to August 2, 2019 to provide an updated abundance estimate in Canadian waters. The surveys were co-designed with Inuvialuit to include all areas identified as potentially within the range of EBS beluga. Due to poor weather during 2019, survey coverage was incomplete. In particular, large portions of the survey design were not completed despite tag data and other lines of evidence indicating beluga presence. To account for belugas that were missed during the survey because they were underwater, surface and dive data collected concurrently from tagged belugas were used to estimate adjustment factors. An estimated abundance of 38,500 belugas (95% CI = 20,700–71,300) was obtained within the survey area after adjusting for belugas that were submerged or missed by observers. Using this estimate and a recovery factor of 1, the Potential Biological Removal (PBR) was calculated as 588 belugas. Due to low survey coverage, the abundance estimate for the population and associated PBR are negatively biased and should be considered underestimates. A United States National Oceanic and Atmospheric Administration (NOAA) aerial marine mammal survey conducted in August 2019 covered offshore areas but did not cover the entire EBS beluga distribution. Although a beluga abundance estimate can be obtained from this survey, it was not included in this assessment as survey data were still being analysed. The EBS population assessment took a collaborative approach with Inuvialuit that engaged participation in the study design, field implementation/execution and the interpretation of findings for the final assessment.
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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 | 0.001 | 0.002 |
| 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) | 0.005 | 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".