Incidence and timing of wild and escaped farmed Atlantic salmon (<i><scp>S</scp>almo salar</i>) in Norwegian rivers inferred from video surveillance monitoring
Bibliographic record
Abstract
Abstract Run timing of escaped farmed Atlantic salmon Salmo salar vs. wild fish was compared by the use of video camera surveillance in 15 rivers over several years, covering 1600 km of the Norwegian coastline (from 58°N to 69°N). Annual runs of wild salmon varied among rivers from <200 fish to more than 10 000. During the surveillance period that for most rivers extended from late May to early October, larger‐sized salmon (fish ≥ 65 cm) generally entered the rivers earlier than small fish. The percentage of salmon identified as escaped farmed fish ranged from 0.1% to 17% across rivers with an average of 4.3%. Estimates of escapees are, however, assumed to represent minimum values because an unknown number of farmed fish passing the video cameras may have been misclassified as wild fish. By the use of a linear mixed model and generalised additive mixed models, it was found that the relationship between run timing and fish length differed significantly between farmed and wild salmon. While small‐sized farmed and wild fish (<65 cm) entered the river at about the same time, wild large salmon returned on average 1–2 weeks earlier than similarly sized escapees. The proportion of large‐sized farmed escapees also increased until late August and decreased thereafter. In contrast, there was a relatively constant and lower proportion of small‐sized escapees throughout the season. Within the surveillance period, there was no evidence of any exceptionally late runs of fish classified as escaped farmed salmon.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 |
| 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 teacher head, 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".