A universal severity classification for natural disasters
Bibliographic record
Abstract
The magnitude of a disaster's severity cannot be easily assessed because there is no global method that provides real magnitudes of natural disaster severity levels. Therefore, a new universal severity classification scheme for natural disasters is developed and is supported by data. This universal system looks at the severity of disasters based on the most influential impact factor and gives a rating from zero to ten: Zero indicates no impact and ten is a worldwide devastation. This universal system is for all types of natural disasters, from lightning strikes to super-volcanic eruptions and everything in between, that occur anywhere in the world at any time. This novel universal severity classification system measures, describes, compares, rates, ranks, and categorizes impacts of disasters quantitatively and qualitatively. The severity index is useful to diverse stakeholder groups, including policy makers, governments, responders, and civilians, by providing clear definitions that help convey the severity levels or severity potential of a disaster. Therefore, this universal system is expected to avoid inconsistencies and to connect severity metrics to generate a clear perception of the degree of an emergency; the system is also expected to improve mutual communication among stakeholder groups. Consequently, the proposed universal system will generate a common communication platform and improve understanding of disaster risk, which aligns with the priority of the Sendai Framework for Disaster Risk Reduction 2015-2030. This research was completed prior to COVID-19, but the pandemic is briefly addressed in the discussion section.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".