Harbour seal population assessment
Bibliographic record
Abstract
Harbour Seals fulfil an important ecological role in the Strait of Georgia (SOG). They are a key prey species for Transient (also known as Bigg’s) Killer Whales and current information on abundance and distribution of Harbour Seals has been identified as an important component of Transient Killer Whale habitat. They are also a major predator of several commercially important fish species in the SOG, including salmon, herring and Hake. Fisheries and Oceans Canada (DFO) has been conducting standardized aerial surveys during the pupping season since the early 1970s to determine Harbour Seal abundance and distribution in Canadian Pacific waters. Harbour Seal populations in the SOG increased exponentially at a rate of about 11.5% during the 1970s and 1980s, and then stabilized in the mid-1990s. Abundance increased from ~3,600 in 1973 to ~39,000 during 1994–2008. Based on surveys flown in 2014, Harbour Seal abundance in the SOG is estimated to have remained stable at ~39,000 (95% CI 35,000–42,100). Although overall numbers are stable in the SOG, there is evidence of continuing redistribution among haulout sites. In addition to ongoing population monitoring, further analysis of changes in Harbour Seal distribution and behaviour are required to support Transient Killer Whale recovery, assess fishery interactions, and identify potential impacts of proposed development in the SOG.
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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 | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".