Tracking industrial fishing activities in African waters from space
Bibliographic record
Abstract
Abstract Marine fisheries in African waters contribute substantially to food security and local economies in African coastal nations. Recently, there are growing concerns about the sustainability of living marine resources in these countries’ exclusive economic zones (EEZs) due to increased risks from climate change, pollution and potential over‐exploitation of fisheries resources by non‐African (foreign) countries. To effectively manage fishing activities and sustain marine resources in African waters, we need useful tools for characterizing the fishing activities in African waters. Here, we assess the utility of the Automatic Identification System (AIS) derived data for describing the spatial characteristics of African and foreign industrial fishing activities within the EEZs of African coastal nations. The results show that the AIS‐derived spatial pattern of industrial fishing activities in African waters is consistent with that of industrial catches derived from the Sea Around Us database. Across African EEZs, the spatial correlations between primary productivity and fishing effort highly vary by gear types, which emphasizes the importance of investigating specific fishing strategies when studying the effects of bottom‐up drivers on fishing effort. We find an EEZ‐specific spatial pattern for fishing efforts across African waters and identify some socioeconomic, political and geographic factors that likely affect the decision of fleets to fish in specific African EEZs. We conclude that AIS‐derived fishing data can be a useful complementary tool for characterizing the spatial pattern of industrial fishing efforts in African waters.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 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".