Trends in Industrial and Artisanal Catch Per Effort in West African Fisheries
Bibliographic record
Abstract
Abstract Artisanal fisheries are generally assumed to generate a lower fishing effort in comparison to the industrial sector. This study aims to comparing catch, fishing effort, and catch‐per‐unit‐of‐effort (CPUE) for each sector, using kWdays as a metric for fishing effort, and kg/kWdays for CPUE. The study, which covers West Africa (1950–2010), finds that the artisanal sector spends 4.7•10 9 kWdays/year versus 1.3•10 9 kWdays/year by the industrial sector, due to increasing numbers and size of artisanal boats, which in Senegal and Ghana can exceed that of (smaller) industrial vessels. The artisanal total fishing effort increased by 10‐fold between 1950 and 2010, in contrast to a decrease in the industrial effort since the 1990s, which points to the occurrence of Malthusian overfishing, a form of fishing that favors excess labor instead of capital. This analysis finds that the CPUE declined by 1/3 since 1950 driven by a strong decline in the artisanal CPUE, which is 11 times lower than industrial CPUE. This confirms other indicators of decline of fish populations. This study calls for the prioritization of artisanal fisheries, with regard to management and data availability, but also as an important but unregulated sector, which contributes to overexploitation of fish stocks that are vital for communities in West Africa.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".