Strategies for the mitigation of environmental impacts from aquaculture: An international comparison
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it