California sea lion population assessment
Bibliographic record
Abstract
Abundance of California Sea Lions overwintering in southern British Columbia (BC) was estimated through five monthly aerial photographic surveys conducted from November 2020 to March 2021. The surveys were restricted to southern BC as few animals have been documented further north. California Sea Lion abundance was also estimated from the 2009-10 and 2017 Steller Sea Lion surveys which covered all sea lion haulouts in BC. To account for sea lions at sea and therefore not photographed during surveys, monthly correction factors derived from satellite tags deployed on sea lions in nearby Puget Sound (Washington, US) between 2014–16, were applied. The surveys flown in winter of 2009–10 and 2017 estimated 4,200 (95% CI 3,600–4,900) and 11,800 (95% CI 9,900–14,000) California Sea Lions, respectively. A mean abundance of 13,600 (95% CI 11,300–16,300) California Sea Lions was estimated in 2020–21. This represents a threefold increase since 2009–10 and no significant increase since 2017. While abundance remained consistent across monthly surveys in 2020–21, there was redistribution of animals. Potential Biological Removal (PBR) for 2020–21 in BC, using a recovery factor of 1.0 and adjusting for the proportion of time California Sea Lions spent in BC, was 433 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| 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) | 0.008 | 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".