Preventive planning model for rescue priority management in seismic emergency
Bibliographic record
Abstract
This study stems from the detection analysis of damaged buildings in Nocera Umbra (Central Italy), which has been affected by the earthquake sequence during 1997-1998, causing serious damages to material goods and human casualties.This natural disaster highlighted the need to achieve a strategic planning during the emergency phase based not only on effective actions but also on the efficient management of rescue and relief operations.The application of Quantitative Risk Analysis (QRA) has led to the seismic risk assessment for estimating the potential level of the expected damage, on heritage buildings and human lives.Data and information have been gathered during the surveys carried out after the major seismic event.A deep analysis has highlighted the existence of a correlation ratio between the critical structural features and the propensity of buildings to collapse.Considering either the exposed population, this information represents the crucial element for a relief management plan.All risk indicators associated to each damage scenario have allowed to assign a scale of priorities at every relief operations for an effective risk mitigation based on damage level and expected casualties.The ex-post emergency organization would be able to make more efficient both allocation and management of resources, equipment and technical teams during rescue activities.This is strongly connected to the ex-ante priority planning of the operations related to magnitude and distribution of expected collapses and casualties thank to the simulation performed to the potential risk scenarios.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".