The impact of tropical cyclones on fishing boats from a global perspective
Bibliographic record
Abstract
This study quantifies the impact of tropical cyclones on fishing boat activity using satellite imagery and tropical cyclone data from 2012 to 2023 across 42 countries. Kernel density estimation identifies key fishing grounds, and impacts are assessed through Generalized Linear Mixed Models. The results reveal significant regional variation in tropical cyclone impacts on fishing activities, with wind speed effects ranging from − 1.17% to + 0.50% change in boat numbers per knot increase. Negative impacts on fishing grounds were mostly in Southeast Asian waters, particularly Indonesia (− 1.22%), the Philippines (− 0.82%), and Myanmar (-0.58%), while positive effects were observed in some fishing grounds of China (+ 0.45%) and Canada (+ 0.45%). Future projections based on predicted cyclone intensification suggest that the Marshall Islands and Vanuatu will experience the highest negative outcomes (− 1.96% and − 1.93%, respectively). The North and South Indian Oceans and the Southwest Pacific reveal overall negative effects across all countries. While North Atlantic countries show consistent positive impact, Northwest countries show mixed impacts. Between 2012 and 2023, countries like Japan, China, and the Philippines recorded the highest total number of TC-affected days, with 205, 188, and 129 days, respectively, highlighting the prolonged disruptions to fishing activities in the Northwest Pacific region.
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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 | 0.000 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".