Ranking of natural disasters in Sri Lanka for mitigation planning
Bibliographic record
Abstract
Purpose The purpose of this paper is to propose a methodology for a priori classification of natural disasters that occur in Sri Lanka, through the development of a set of weighted parameters based on the product of the disaster impact and the affected area, in order to prepare mitigation plans. Design/methodology/approach Experts' opinions were used for developing the parameters. Through a facilitated workshop, the weights of the disasters were obtained from experts involved in disaster mitigation at the local, regional and national levels in Sri Lanka. A correlation analysis was used to determine the most appropriate independent measures of disaster impact and affected area, the product of which was used to rank the identified disasters for further action. Findings For the pre‐selection of major disasters, the study showcases four weighted parameters, one of which is identified as the best. In total, five disasters have been singled out for further consideration in Sri Lanka. The product of the affected area factor, based on administrative area classification, and the impact factor, out of the two considered, that places a higher weight on minor disasters, is shown to be the best criterion. Research limitations/implications The geographical distribution of the participants (experts) does influence the results, and those available for the workshop were not fully representative of all Sri Lanka's provinces. Originality/value The paper emphasizes the importance of the consideration of the area impacted rather than the classification, which is based solely on the severity of the impact. The categorization of disasters based on experts' opinions and the related analysis revealed a priority order for planning for certain identified disasters.
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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 | 0.001 | 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 | 0.001 | 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".