Assessing Guinea Bissau's Legal and Illegal Unreported and Unregulated Fisheries and the Surveillance Efforts to Tackle Them
Bibliographic record
Abstract
Fisheries in Guinea Bissau contribute greatly to the economy and food security of its people. Yet, as the ability of the country to monitor its fisheries is at most weak, and confronted with a heavy foreign fleet presence, the impact of industrial foreign fleets on fisheries catches is unaccounted for in the region. However, their footprint in terms of catch and value on the small-scale sector is heavily felt, through declining availability of fish. Fisheries in Guinea Bissau are operated by both legal (small-scale and industrial), and illegal (foreign unauthorized) fleets, whose catches are barely recorded. In this paper, we assess catches by both the legal and illegal sector, and the economic loss generated by illegal fisheries in the country, then attempt to evaluate the effectiveness of Monitoring Control and Surveillance (MCS) of Guinea Bissau’s fisheries. Two main sectors were identified through official reports and a literature review, the large-scale (industrial) sector, which between 2011 and 2017 included exclusively catches by foreign owned and flagged vessels, and catches by the small-scale sector, which remain largely unmonitored in official statistics. We use the available data on the number of legal and illegal vessels and/or fishers, and their respective catch per unit of effort to estimate catches, and we analyze monitoring outcomes against the registered industrial and artisanal fleets. We find that of the legal industrial vessels, 20% were linked to criminal activities in the past 7 years. These activities range widely from using an illegal mesh size, to fishing in a prohibited area, to labor abuse and drug trafficking. Overall, total small-scale and industrial catches were estimated at 370,000 t/year in 2017, of which less than 2% is ever reported to the FAO. Small-scale catches represented 8% of the total catch, and this contribution was found to be declining. Industrial fisheries generate over $458 million US, or which $75 million US is taken illegally, falling under the category trans-national fisheries crimes. The slight negative relationship between the number of monitoring days at sea illegal catches suggests increasing MCS efforts may play an important role in reducing illegal fishing in the country.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".