Can fisheries yield be enhanced by large-scale feeding of a predatory fish stock? A case study of the Icelandic cod stock
Bibliographic record
Abstract
No abstracts are to be cited without prior reference to the author.The idea to feed predatory fish to enhance fisheries yield is introduced and evaluated for the Icelandic cod stock. The benefit from large-scale feeding may be two-fold. First, it may increase the growth rate of a predatory fish stock. Second, it may reduce cannibalism and predation on valuable species. For a large part of the year there is limited overlap in the distribution of the Icelandic cod stock and adult capelin, its principal prey. This may result in starvation, reduced growth, cannibalism and predation on expensive prey. For large-scale feedingto be economically feasible it is necessary to have access to large quantities of inexpensive feed and an efficient feeding technique. In Iceland about 1 million tons of capelin' and herring are landed annually for fishmeal production. It seems likely that the basic feeding technique used in a small-scale feeding experiment in an Icelandic fjord can be scaled up for largescale feeding. Five different feeding scenarios and the research required for. feasibility assessment are considered for the Icelandic cod stock.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".