Population status assessment and potential biological removal (PBR) for the Atlantic Harbour Seal (Phoca vitulina vitulina) in Canadian waters
Bibliographic record
Abstract
Aerial surveys were conducted between June and August of 2019–21 to assess harbour seal (Phoca vitulina vitulina) abundance and distribution in Atlantic Canada. The surveys covered three regions: the Gulf of St. Lawrence (GSL), the Scotian Shelf (SS), and the Newfoundland and Labrador Shelves (NLS). These surveys counted seals at haul-out sites in each region. A total of 10,327 individuals were counted, with 55%, 23%, and 22% of the individuals counted in the GSL, SS, and NLS, respectively. To estimate abundance, these counts were adjusted for the proportion of seals which were at sea during the time of the survey and were, therefore, unavailable to be counted. We applied correction factors (CF) of 2.55 (coefficient of variation [CV]: 16.02%) and 1.64 (CV: 8.67%) for surveys taking place during the pupping and moulting periods, respectively, as developed in a companion study based on recent telemetry data and CFs reported in the literature. Applying these CFs to survey counts yielded a total estimated harbour seal abundance for Atlantic Canadian waters for 2019–21 of 25,183 individuals (95% CI 22,548–28,126). The GSL, SS, and NLS regions accounted for 58%, 24%, and 19% of the total estimated abundance, respectively. These are likely minimum estimates as a result of seals entering the water prior to the count due to disturbance, imperfect detection of hauled out seals, and a lack of coverage in some areas. The uncertainty around the abundance estimate may also be underestimated since there was no reported uncertainty in survey counts and data to develop CFs for the study area were limited. The Potential Biological Removal (PBR) for Atlantic Canada’s harbour seal population, based on this abundance estimate, was 720 individuals.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".