Bibliographic record
Abstract
This thesis contains three essays on Extreme Weather and the Economics of Fisheries. The first essay, Marine Heatwaves and Commercial Fishing in New Zealand, investigates the influence of marine heatwaves (MHWs) on fish catch within the ocean in New Zealand’s Exclusive Economic Zone. MHWs, characterized by short-term sea-surface temperature (SST) anomalies, are becoming more frequent due to anthropogenic climate change. Using high-resolution SST and fish catch data, the study applies Geographically and Temporally Weighted Regression to analyse the spatially heterogeneous impacts of MHWs. The results show that moderate MHWs are associated with increased fish catches; however, intense MHWs lead to significant declines, disrupting fish populations and habitats. This chapter highlights the economic and ecological vulnerabilities posed by the intensifying MHWs and emphasizes the need for adaptive fisheries management to ensure sustainability. The second essay, Impact of Tropical Cyclones on Fishing Activity in the Philippines, identifies the main fishing grounds within the Philippine Exclusive Economic Zone (EEZ) and examines the response of fishing vessels to tropical cyclones (TCs) in 2012. Using satellite imagery from NOAA and TC data from the International Best Track Archive for Climate Stewardship, this study employs kernel density estimation to map fishing grounds and advanced regression techniques to analyse the effect of TC speed on fishing activity. Results indicate an overall negative impact of TCs on vessel activity during and two days after their passage, with significant reductions in the Sibuyan Sea, Visayan Sea, and Panay Gulf. Daily commercial fishing production in Western Visayas was estimated to decline by 7,800 tons, affecting over 188,000 families. These findings highlight the vulnerability of fisheries to TCs and the broader socio-economic consequences for coastal communities. The third essay, The Impact of Tropical Cyclones on Fishing Boats: Global Perspective, quantifies the effects of TCs on fishing activity globally from 2012 to 2023 across 42 countries. Using satellite imagery and tropical cyclone data, the study applies kernel density estimation to identify fishing grounds and Generalized Linear Mixed Models to assess TC impacts. The analysis reveals considerable regional variation, with wind speed effects ranging from -1.17% to +0.50% change in boat numbers per knot increase. Southeast Asian countries, including Indonesia (-1.22%), the Philippines (-0.82%), and Myanmar (-0.58%), experienced the most significant negative impacts, while positive effects were observed in fishing grounds of China (+0.45%) and Canada (+0.45%). Future projections suggest severe impacts for the Marshall Islands (-1.96%) and Vanuatu (-1.93%), highlighting the vulnerability of these regions to TC intensification. The study also reports that Japan, China, and the Philippines recorded the highest number of TC-affected days in their EEZ (205, 188, and 129, respectively), indicating prolonged disruptions to fishing activities in the Northwest Pacific.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".