Prediction of biomass in Norwegian fish farms
Bibliographic record
Abstract
We have constructed a statistical model to forecast, with uncertainty, the stock of Norwegian farmed Atlantic salmon ( Salmo salar ). The model provided good predictions of future biomass of Norwegian farmed salmon and can also be used to perform “what-if” analysis exploring the impact of varying scenarios for stocking and slaughtering. The model is based on the number of fish in each mass class (0–1, 1–2, …, 10+ kg) and their average mass. The model, which is related to standard size-structured models, computes the number of fish growing into the next mass class the next month and the number of fish remaining in the same mass class. In addition, the number of new fish stocked, fish lost, slaughtered, and wasted, as well as the sea temperature related to the growth, were modelled. All the model parameters were estimated based on monthly data from 2002 to 2007, and the model was validated statistically. Any animal production involving cycles may benefit from this forecasting tool.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".