A Template for Urban Management of Biological Exposures and Casualties
Bibliographic record
Abstract
Background: This study evaluated the non-structural elements of the medical capacity available following the Ji-Ji earthquake.This catastrophic earthquakes registering 7.3 on the Richter scale, struck mid-Taiwan on 21 September 1999, and took a death toll of 2,403, and injured 10,002 persons.Methods: Four affected hospitals participated in the study.Affected hospitals were defined as those with at least 200 beds that were within the epicenter area.The damaged, non-structural elements of these evacuated hospitals were examined and scored.Results: These hospitals suffered from only minor structural damage, but sustained extensive non-structural damage and were forced to evacuate patients from their buildings.Several major operational and functional components (OFC) that were critical to their operations were damaged: falling objects, flooding, loss of electricity, and damaged medical equipment.Conclusion: A well-designed, disaster medical care system should include seismic considerations of these hospitals, especially those key non-structural elements evaluated.In the 1999 Taiwan Ji-Ji quake, these affected hospitals lost most of their medical capacity at a period when patients desperately needed medical attention.It is important to reestablish the advanced design code for the repaired hospitals, providing OFC seismic protection to reduce mortality in next rural temblor.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.037 | 0.014 |
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".