A genetic analysis of sex-specific prey consumption in harbor seals (Phoca vitulina) and the implications for commercially important fish species in the Pacific Northwest.
Bibliographic record
Abstract
Formerly abundant salmonid, rockfish, and herring species are experiencing population decline in the Salish Sea. To design effective management and recovery strategies, we must understand the population dynamics of these fishes in relation to their predator species: the harbor seal (Phoca vitulina). Given that food consumption in the dimorphic harbor seal is related to body mass, an understanding of prey population dynamics requires a description of sex-specific diet preferences. Genetic barcoding was performed on seal scat to generate diet composition data; qPCR was used to identify zinc finger proteins (ZfX and ZfY) to determine the sex of the individual that deposited each sample. Data were collected from two haul-out sites in 2012-2013 in the Georgia Strait, Canada. At Comox during 2012, male harbor seals (n=82 scats) consumed Pacific Hake (37%), salmon species (26%), and Pacific Herring (13%), whereas females (n=68 scats) consumed Pacific Herring (35%), Pacific Hake (8%), salmon species (8%), Pacific Staghorn Sculpin (7%), and Lingcod (5%). Given that Lingcod and sculpins are salmon predators, similar results following complete analysis would indicate that female harbor seals may improve salmon recovery whereas males may diminish it. Findings also highlight the importance of addressing intra-specific differences to understand community interactions and suggest management strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".