Early casualty estimates and medical help management after the M7.3 Kermanshah earthquake of November 12, 2017 in Iran
Bibliographic record
Abstract
Medical responses to fatal earthquakes have to be rapid to save lives. Here we report the QLARM alert that was issued less than an hour after the magnitude 7.3 Kermanshah, Iran, earthquake of 2017 and the following medical response. The near-real-time estimates of fatalities were 520, on average, and it took official and news reports about 2 days to settle on a minimum of 630 fatalities as a final count. The response of various Iranian agencies was rapid and effective, facilitated by the relatively small area of the disaster (radius of about 50 km). Although this disaster was not large enough to require international first responders to rush to the scene, it is clear that in very large earthquake disasters, a fast, accurately informed response saves lives. For international teams to be of optimal use, the locations and functionality levels of health facilities should be known. This information could be included in the earthquake alerts, but the necessary worldwide data on hospitals are currently not available.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".