Impacts of anthropogenic and natural “extreme events” on global fisheries
Bibliographic record
Abstract
Abstract A broad range of extreme events can affect fisheries catch and hence performance. Using a compiled database of extreme events for all maritime countries in the world between 1950 to 2010, we estimate effects on national fisheries catches, by sector, large‐scale industrial and small scale (artisanal, subsistence and recreational). Contrary to general expectations, fisheries catches respond positively to nearly all forms of extreme events, suggesting a valuable coping or compensation mechanism for coastal communities as they increase their catch after extreme events, but also an opportunistic behaviour by foreign industrial fishing fleets, as industrial catches increase. These effects vary according to country characteristics, with lower coping capacity for coastal communities and higher opportunistic fishing by foreign fleets in countries with poor governance, higher unemployment and direct exposure to prolonged armed conflicts. We also observe an accumulative effect resulting from the aggregation of multiple disasters that deserves further consideration for disaster mitigation. These findings may assist with managing fisheries towards increasing resilience and adaptive capacity such as early detection of potential impacts, protecting livelihoods and food sources, preventing illegal fishing by industrial fleets and informing aid responses towards recovery.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".