Strategies for the mitigation of environmental impacts from aquaculture: An international comparison
Bibliographic record
Abstract
This research project was conducted to analyse and compare the environmental effectiveness, economic efficiency, fairness and simplicity of two policies supporting the reduction of the environmental impacts from the farming of Atlantic salmon in Canada and in Norway. Reduction of biodiversity loss, potentially caused by aquaculture, has led to new regulations by governments. In Canada, licenses impose quality standards for the installation and the equipment that must be used in aquaculture facilities. Detailed maintenance routines must also regularly be made on the equipment. These measures should reduce fish escapes and reduce biodiversity loss. In Norway, the Ministry may establish protected areas for wild Atlantic salmon populations, preventing aquaculture activities from occurring within the boundaries of these areas. Norway’s longer history and higher production might suggest a policy with greater environmentally effectiveness, economic efficiency, fairness and simplicity. However, the comparison suggested that both countries have policies that are not based on sufficient scientific evidence to support strong environmental effectiveness, although Canada’s is slightly higher than Norway. Furthermore, while both policies have similar economic efficiency, the Canadian one is fairer and it has greater simplicity. Overall, the poor weight of evidence supporting the environmental effectiveness of both policies suggests that governments should probably promote policies that define an end goal rather than the methods to achieve a particular goal. This might encourage the industry to take greater responsibility and adopt adaptive management strategies.
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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 | 0.013 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".