Sea cage aquaculture affects distribution of wild fish at large spatial scales
Bibliographic record
Abstract
Aquaculture sea cages are fixed in space and inadvertently provide food to wild animals that is stable through time. We measured the effect of these novel and highly predictable resource patches on the distribution of wild fish across large spatial scales along the south coast of Newfoundland, Canada. Randomized stratified hydroacoustic surveys were used to compare the distribution and abundance of wild fish in bays that contained Atlantic salmon (Salmo salar) farms with control bays. Control bays were areas with no history of salmon farming but have been selected for future use by the industry. We found that measures of total area backscatter (nautical area scattering coefficient, NASC) were significantly greater in bays that contain salmon farming compared with control locations. The mean NASC in farmed bays was not significantly different from mean NASC measurements taken directly adjacent to sea cages. Variability around mean NASC estimates could not be explained by the quantity of feed available to consumers, when the number of sea cages in a farm site was used as a proxy for feed availability. Our results suggest that individual-level consumer responses at sea cages can be transmitted across larger spatial scales.
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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 | 0.000 | 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".