Salmon lice biology, environmental factors, and smolt behaviour with implications for the Norwegian salmon farming management system: A critical review
Bibliographic record
Abstract
Abstract In 2017, a new regulatory management system, the traffic light system (TLS), was implemented to estimate the effects of salmon lice from Norwegian salmon aquaculture on marine survival of wild Atlantic salmon and forms the basis of aquaculture capacity regulation. The TLS relies on observational data and a set of models to estimate the risk for negative impact on wild salmon populations. This review of the literature that forms the basis for the TLS as well as other relevant studies is presented in the context of the currently practiced TLS and suggestions are made for immediate and long‐term improvements. The main findings of this review are that: (1) assumed timing and duration of smolt migration contribute to unreliable observational and modelled data and overestimates of infection pressure; (2) production of lice larvae from farmed salmon is overestimated; (3) TLS model systems rely on or are calibrated by the same potentially flawed data; (4) lice‐associated mortality in wild salmon smolts may be overestimated; and (5) lice infection levels on farms are not associated with measurable effects on wild salmon. Recommendations to improve the accuracy and reliability of the TLS, and hence its environmental efficiency include the more complete use of available biological and physical environmental variables, adjusting the time period that observational data are registered and modelled data are integrated, adjusting the interpretation of data including recognition of uncertainty in model outcomes, and use of more realistic assumptions concerning lice‐induced mortality thresholds.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".