Availability and usefulness of economic data on the effects of aquaculture: a North Atlantic comparative assessment
Bibliographic record
Abstract
Abstract This paper focuses on the availability of economic indicators and metrics to assess effects of marine aquaculture production in the North Atlantic area (the EU, Norway, Canada and USA), including also social and environmental effects. We consider how aquaculture planning and management is organised in the different countries and the usefulness of economic information to address different aquaculture‐related policies. We find that the most relevant economic data for aquaculture management should be at the local and regional levels rather than nationally. The availability of such economic data is mapped for national, regional and local level. The focus is on data that are publicly available from authorities or research institutions. The availability of data is generally fairly good for national and regional data on the direct economic effects of aquaculture. Data on how aquaculture‐related products or input markets are affected are however poorly available, as are economic data on external effects from aquaculture. Countries with a larger aquaculture sector tend to have better availability of aquaculture‐related economic data than those with a smaller sector. An index is developed and calculated to show more specifically where the countries have relatively good or poor data availability compared to their needs. While it will not always be cost‐effective or meaningful to collect economic data on the effects of aquaculture, our study indicates that several countries could benefit from expanding such data collection. It can make trade‐off decisions more consistent and easier to perform, and aquaculture policies and measures can be better tailored to specific contexts.
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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 | 0.022 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".