Scoping a Public Health Impact Assessment of Aquaculture with Particular Reference to Tilapia in the UK
Bibliographic record
Abstract
Background. The paper explores shaping public health impact assessment tools for tilapia, a novel emergent aquaculture sector in the UK. This Research Council’s UK Rural Economy and Land Use project embraces technical, public health, and marketing perspectives scoping tools to assess possible impacts of the activity. Globally, aquaculture produced over 65 million tonnes of food in 2008 and will grow significantly requiring apposite global public health impact assessment tools. Methods. Quantitative and qualitative methods incorporated data from a tridisciplinary literature. Holistic tools scoped tilapia farming impact assessments. Laboratory-based tilapia production generated data on impacts in UK and Thailand along with 11 UK focus groups involving 90 consumers, 30 interviews and site visits, 9 visits to UK tilapia growers and 2 in The Netherlands. Results. The feasibility, challenges, strengths, and weaknesses of creating a tilapia Public Health Impact Assessment are analysed. Occupational and environmental health benefits and risks attached to tilapia production were identified. Conclusions. Scoping public health impacts of tilapia production is possible at different levels and forms for producers, retailers, consumers, civil society and governmental bodies that may contribute to complex and interrelated public health assessments of aquaculture projects. Our assessment framework constitutes an innovatory perspective in the field.
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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 | 0.021 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.025 | 0.017 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".