The influence of siting and deterrence methods on seal predation at Atlantic salmon (<i>Salmo salar</i>) farms in Maine, 2001–2003
Bibliographic record
Abstract
We document the nature and frequency of seal predation at Atlantic salmon (Salmo salar) farms in Maine and determine whether the severity of predation is related to the proximity of farms from one another and nearby harbor seal (Phoca vitulina concolor) haul-outs. We surveyed farm managers annually from 2001–2003 to document management techniques, husbandry practices, and predator deterrence methods employed for comparison with the extent of seal predation. Biweekly aerial surveys were conducted between January and March of each year to document harbor seal presence. An empirical estimate from a negative binomial model showed seal predation at farms declined significantly with distance to the nearest haul-out, suggesting that seal predation may be deterred by maximizing the distance between farms and seal haul-outs. Farms located further than 4 km from harbor seal haul-outs experienced minimal losses. At farms located within 4 km of harbor seal haul-outs, seal predation decreased with increasing distance from neighboring farms, indicating that areas where farms are concentrated may be more vulnerable. The regular replacement of primary and secondary cage netting was negatively correlated with seal predation. Finally, this study documents the apparent ineffectiveness of acoustic harassment devices at deterring seal predation.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".