Differences between Disaster Prediction and Risk Assessment in Natural Disasters
Bibliographic record
Abstract
ABSTRACT A clear distinction between disaster prediction and risk assessment is necessary for effective disaster reduction. Disaster prediction models objects that face hazard, damage, or loss, while risk assessment models the likelihoods of the scene in future adverse incidents. In terms of mathematics, a model for disaster prediction can be an explicit function, while a model for risk assessment might be an implicit function. There are at least three criteria to judge whether a model is suitable for risk assessment: (i) available information is incomplete, (ii) the scene in the future is very uncertain, and (iii) the model depends on comparing the current situation with some known patterns. In this article, we use the case of an earthquake to show the difference between disaster prediction and risk assessment. Key Words: disaster predictionrisk assessmentprobabilitysceneearthquake. ACKNOWLEDGMENTS The author is indebted to anonymous referees for their valuable comments for the revision of this article. This work was supported by National Natural Science Foundation of China (No. 40771007), National Key Project of Scientific and Technical Supporting Programs Funded by Ministry of Science & Technology of China (No. 2006BAD20B01-02), and The National High Technology Research and Development Program of China (No. 2009AA12Z124).
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".