Catching industrial fishing incursions into inshore waters of Africa from space
Bibliographic record
Abstract
Abstract Small‐scale fisheries contribute substantially to the sustainability of coastal communities by providing livelihood and economic opportunities and ensuring food security. However, their geographic range of operation overlaps with that of industrial fisheries, increasing the resource competition, risk of vessel collision and inter‐sector conflicts, while jeopardizing the sustainability of fish stocks. When industrial vessels venture into waters that are reserved to artisanal fisheries, their operations become illegal. In Africa, the extent of such operations, beyond their legal implications, has resulted in severe economic, food security and maritime safety issues. In this paper, we use automatic identification system data derived from satellite technology to predict fishing operations and find that industrial fleets spend 3%–6% of their time fishing within inshore areas reserved for small‐scale fisheries between 2012 and 2016, of the total 4.2 million industrial fishing hours within the Exclusive Economic Zones of African countries. We assessed the total fishing effort by this form of illegal fishing operations at 166 million kWhours at least out of 4.9 billion kWhours in total. We discuss this dangerous form of illegal fishing, which often results in deadly collisions with small‐scale sector operators, increases competition and conflicts over fisheries access, threatens the sustainability of fish stocks, and calls for better governance, and protection.
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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 | 0.001 |
| 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".