Land use and land cover play weak roles in typhoon economic losses at the county level
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Many empirical typhoon economic loss models consider that the losses caused by typhoons mainly depend on the intensity of the hazards and the exposure in the affected areas. Few studies have attracted attention to the role of disaster-formative environmental factors in typhoon losses. In this study, we chose land use and land cover (LULC) as disaster-formative environmental factors together with typhoon wind speed, rainfall, and gross domestic product (GDP) as predictive factors for typhoon economic losses in Guangdong Province, China. The results showed that the intensity of wind speed was the most important factor, while LULC played weak roles in typhoon economic losses for 23 typical typhoons in terms of county level losses in Guangdong. Subregionally, typhoon economic loss models performed better in coastal areas than in noncoastal areas.
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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 | 0.000 |
| 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) | 0.332 | 0.004 |
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 it