Size matters: fishing less and yielding more in smaller-scale fisheries
Bibliographic record
Abstract
Abstract Several factors influence catches and the sustainability of fisheries, and such factors might be different depending on the scale on which fisheries work. We investigated the existence of possible subdivisions within small-scale fisheries (SSF) themselves, regarding their economic performance and relative social and environmental impacts to understand which categories of these two types of fleets are best positioned to support sustainability. By doing so, we investigated if it is a good strategy for SSF to aim to grow towards larger scales. We obtained economic and ecological data from landing samplings and information on technological efficiency of this fleet, using a northeastern Brazilian state as a case study. We defined a cut-off point to separate the SSF into two categories of boats, according to their size and gear. We compared their cpue and the factors affecting it within each category; we also compared economic (number of boats, number of landings, jobs, gears, catch, travel time and total time of the fishery, revenues, costs, profits, revenue per unit of effort, and profit per unit of effort) and ecological factors (vulnerability of species caught) between the two categories. We found that small boats spent less time fishing and employed comparatively more people per landed value and catch. The cpue and profits of small boats were also higher. Both large and small boats exploit species with the same overall vulnerability. Therefore, being smaller, even within the SSF category, seems to be a more advantageous social and economic strategy for guaranteeing higher catches and more employment opportunities per catch. These findings need to be taken into account when defining new policies, such as the distribution of subsidies that support or not the sustainable use of fishery resources.
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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 | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".