A Study of Disaster Insurance in Extreme Weather Based on Logistic Regression and ARIMA Models
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
This paper examines the impact of extreme weather events on insurance under-writing decisions and real estate development. Using a combination of statistical mod-els, including Topsis entropy weight method and Logistic regression, we analyze the correlation between extreme weather indicators and insurance claims. Building upon this analysis, we investigate the factors influencing housing sales rates and forecast disaster losses using the ARIMA model. By treating the housing sales rate as a proxy for insurance compensation rates, we refine the insurance claims and profit model, providing insights for insurance underwriting decisions in different regions. Our findings offer new perspectives on mitigating risks and optimizing insurance policies in the face of changing environmental and social factors.
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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 | 0.000 |
| 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 it