Bibliographic record
Abstract
The Pacific harbour seal stock in Canada is composed of two populations which are assessed as a single stock. As done previously, coast-wide abundance of harbour seals in BC was assessed through aerial surveys conducted over multiple years, and corrected for area covered, survey timing relative to peak pupping, and proportion of seals hauled out during surveys. To estimate the proportion of seals hauled out during survey conditions, satellite tags were deployed on 30 seals in the Strait of Georgia during 2019-2021. A haulout correction factor of 1.27 (CV=0.02) was applied to counts from the 2015-2019 surveys. This differs from the correction factor of 1.63 (CV=0.042) which was applied to counts prior to 2015. During 2015-2019, over 90% of the BC coast was surveyed, resulting in an estimated 85,400 (95% confidence interval 82,000-88,900) harbour seals. Over 40% of the seals were found in the Strait of Georgia. The total abundance of harbour seals in BC was re-estimated for the 2003-2008 period at 112,400 (95% CI 108,000-117,000) to better account for lower survey coverage. The updated estimate remains within the range of the original estimate of 105,000 (95% CI 90,900-118,900) and is significantly higher that the most recent estimate. The abundance of BC harbour seals increased from a low of approximately 10,000 in the 1960’s to over 100,000 seals in the early 2000’s, and is now stable or slightly declining. Abundance, density and trends varied regionally. Based on the estimated coast-wide abundance in 2019, Potential Biological Removal (PBR) for the BC stock is 5,045 seals. PBR was also estimated for the seven survey regions in their respective survey year. Although survey regions do not represent distinct populations, managing at a more local scale can help avoid local depletion. Accounting for regional abundance and trends, the estimates for regional PBRs are: Strait of Georgia =2,069, west coast of Vancouver Island=711, Queen Charlotte Strait=147, Discovery Passage=18, central mainland coast =58, northern mainland coast=581, Haida Gwaii=418. The proportion of animals not seen during the survey (haulout correction factor) has a large impact on the abundance estimate. The current correction factor, which is derived from the Strait of Georgia, may not be applicable to all regions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".