Disentangling the role of sea lice on the marine survival of Atlantic salmon
Bibliographic record
Abstract
Abstract The effects of sea lice on the marine survival of wild salmonids are widely debated. In Norway this debate has reached a crescendo as the Norwegian government has recently ratified a management system where the growth in the salmonid aquaculture industry will be conditional on regional estimated impact of salmon lice on wild fish. Sea lice have thus become the most prominent obstacle to the stated political aim of quintupling aquaculture production in Norway by 2050. Scientific documentation that salmon lice impact the marine survival of salmon is robust. However, it is also evident that marine survival of salmon is strongly impacted by other factors, and that the effect of salmon lice is most likely an integral part of these other mortality factors. In this paper, our goal is to discuss and give advice on how managers and policy makers should handle this complexity, and to identify the greatest challenges in using scientific results to construct robust management rules. Inadequate extrapolation from the scale of known effects to the scale of management implementation may initially give a false impression of scientific certainty, but will eventually fuel upsetting disagreements among stakeholders as they gradually uncover the shaky foundation of the implemented policy. Thus, using a single model and parameter to determine management advice is not warranted, as no single data point reflects the natural complexity of nature. Furthermore, robust management rules should be based on unambiguous definitions of key concepts. Finally, despite the scientific consensus that salmon lice are a risk to salmon, studies on wild populations in situ that accurately quantify the impact of salmon lice are still urgently needed. We give advice on how this can be accomplished.
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 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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".