Sex Ratios of Harbor Seal (Phoca vitulina) Haul Out Sites in the Salish Sea
Bibliographic record
Abstract
Harbor seals (Phoca vitulina) are the most abundant marine mammals in the Salish Sea, with frequent interactions with humans and predation on declining Pacific salmon (Oncorhynchus spp.) populations. We evaluated proximity to both human disturbance and prey availability relative to sex ratio of 14 haul out sites in the Salish Sea. Due to higher adult salmon proportion in diet and potentially higher risk-tolerance, we hypothesized that male dominated haul out sites would be found in areas of high human disturbance and closer to salmon runs. As a proxy for human disturbance, we collected data on marina locations and accounted for areas with vessel traffic. From public data of Washington Department of Fish and Wildlife and Canada’s New Salmon Escapement Database System, we compiled salmon run abundance and location data. Coordinate, area and abundance data were mapped with ArcGIS Pro to extract the proximities of marinas and salmon runs to each haul out site. All male dominated sites were < 3 km to the nearest marina, while all female dominated and evenly split sites were > 3 km, except Gertrude Island. No clear relationship was found between sex ratios and proximity to salmon runs. Density of salmon runs or marinas showed no clear relationship with haul out sites. Data indicate that, unlike males, female harbor seals prefer haul out sites further from human disturbance. We suggest that future pinniped management should consider increased risk-tolerance of male harbor seals when evaluating the Salish Sea populations through scat genetics or survey counts. (As the paper for this study will be submitted elsewhere, a personal reflection will act as a placeholder until the published research paper can be linked)
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".