Diversity of fishing <i>métier</i> use can affect incomes and costs in small-scale fisheries
Bibliographic record
Abstract
The implementation of an ecosystem-based approach to fisheries management in multispecies fleets has the potential to increase fleet diversification strategies, which can reduce pressure on overexploited stocks. However, diversification may reduce the economic performance of individual vessels and lead to unforeseen outcomes. We studied the economic performance of different fleet segments and their fishing métiers in Wales (United Kingdom) to understand how the number of the métiers employed affects fishing income, operating costs, and profit. For the small-scale segment, more specialised fishers are more profitable and the diversity of métiers is limiting both the maximum expected income and profit but also the operating costs. This last result may explain the propensity of fishers to increase the number of métiers for at least part of the studied fleet. Therefore, while for some vessels, increasing the diversity of fishing métiers may be perceived to limit economic risk associated with the interannual variability of catches and prices and (or) to reduce their operating costs, it can ultimately result in less profitable activity than more specialised vessels.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".