Towards Integration of Environmental and Health Impact Assessments for Wild Capture Fishing and Farmed Fish with Particular Reference to Public Health and Occupational Health Dimensions
Bibliographic record
Abstract
The paper offers a review and commentary, with particular reference to the production of fish from wild capture fisheries and aquaculture, on neglected aspects of health impact assessments which are viewed by a range of international and national health bodies and development agencies as valuable and necessary project tools. Assessments sometimes include environmental health impact assessments but rarely include specific occupational health and safety impact assessments especially integrated into a wider public health assessment. This is in contrast to the extensive application of environmental impact assessments to fishing and the comparatively large body of research now generated on the public health effects of eating fish. The value of expanding and applying the broader assessments would be considerable because in 2004 the United Nations Food and Agriculture Organization reports there were 41,408,000 people in the total 'fishing' sector including 11,289,000 in aquaculture. The paper explores some of the complex interactions that occur with regard to fishing activities and proposes the wider adoption of health impact assessment tools in these neglected sectors through an integrated public health impact assessment tool.
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.
How this classification was reachedexpand
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".