Stock assessment of Atlantic Harbour Seals (Phoca vitulina vitulina) in Canada for 2019–2021
Bibliographic record
Abstract
Aerial surveys to obtain counts of harbour seals were conducted in the Gulf of St. Lawrence (GSL, 2019), on the Scotian Shelf (SS, 2020), and on the Newfoundland and Labrador Shelves (NLS, 2021). A total of 10,327 seals were counted on haul-out sites, with 55%, 23%, and 22% of the seals counted in the GSL, SS, and NLS, respectively. To estimate abundance, counts were adjusted for the estimated proportion of seals which were at sea during the time of the surveys and were, therefore, unavailable to be counted. Correction factors were derived by combining recent data on haul-out behaviour of harbour seals in the St. Lawrence Estuary and haul-out correction factors reported in the literature. Haul-out correction factors of 2.55 (CV: 16.02%) and 1.64 (CV: 8.67%) for surveys flown during the pupping and moulting periods, respectively, were applied to the survey counts. The total estimated harbour seal abundance for Atlantic Canadian waters in 2019–21 was 25,200 individuals (95% CI 22,500–28,100; rounded to the nearest 100). The GSL, SS, and NLS accounted for 58%, 24%, and 19% of this total, respectively. Based on the estimated total abundance for 2019–21, the annual Potential Biological Removal (PBR) is 720 seals. The haul-out correction factors have a large impact on the abundance estimate but are informed by a limited amount of data on haul out behaviour in Atlantic Canada.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".