The hydrodynamic foundation for salmon lice dispersion modeling along the Norwegian coast
Bibliographic record
Abstract
Abstract Norway has complicated dynamics in the coastal ocean and in the fjords. In this area is also the largest salmon aquaculture industry in the world. The salmon industry is valuable for Norwegian economy worth more than 60 billion NOK. Thus, it is important to know the physical oceanography along the coast, even variability on short temporal and spatial scales (h/km), to be able to quantify environmental effects of the aquaculture industry. This is the motivation behind the implementation of a current model covering the whole coast of Norway with a relatively high spatial grid size of 800 m. The NorKyst800 is an implementation of the ROMS current model with an elaborated system of forcing and boundary conditions. This model has an important role for Norwegian authorities in various management purposes. We show that the NorKyst800 results are realistic and typically deviating at most by 1 °C and one unit in salinity from observations. The currents in the upper 10–20 m of the water column vary in a similar way as observed current and the agreement is good. The usefulness of a tool like the NorKyst800 is illustrated by an example of dispersion of salmon lice which is the biggest problem the salmon industry presently is facing. Detailed information, as can be provided by NorKyst800, is needed to fully understand and quantify environmental effects of the aquaculture industry. Similar modeling systems describing the planktonic salmon lice concentration operationally could be beneficial also in other salmon-producing countries like Scotland, Canada, or Chile. The major requirement will be access to updated number of fish and female lice per fish on a weekly time scale.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".