Predicting the impacts of escaped farmed Atlantic salmon on wild salmon populations
Bibliographic record
Abstract
The escape of Atlantic salmon (Salmo salar) from aquaculture facilities can result in both negative genetic and ecological interactions with wild populations, yet the ability to predict the associated risk to wild populations has remained elusive. Here we assess the potential of a spatiotemporal database of aquaculture facility locations, production estimates, and escape events to predict the distribution of escaped farmed salmon and genetic impacts on wild populations in the Northwest Atlantic. Industry production data, reported escape events, and in-river detections of escaped farmed salmon were collected from across the Northwest Atlantic. Genetic estimates of impact were obtained using single nucleotide polymorphisms (95 loci) representing aquaculture and wild salmon throughout the region (30 populations, 3048 individuals). Both the number of escaped farmed salmon detected at counting facilities and the magnitude of genetic impacts were positively correlated with a cumulative spatial measure of aquaculture production. Our results suggest that the risk of escapees and genetic introgression from wild–farmed salmon interactions can be assessed using information on farm production characteristics. This represents a first step in predicting the impact of existing cage-based farms on wild Atlantic salmon.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".