Bibliographic record
Abstract
In emergencies, assessment and communication activities are particularly important for the support of the top decision-making bodies, to evaluate “just in time” the best actions to be taken. The multiple problems to be solved require specific skills in different areas. Upon the occurrence of a calamity, the authorities must answer questions such as “is a given place safe from the threat (e.g., an oncoming flood)?”, that’s why today knowledge of tools that can support decision-making is increasingly necessary: the so-called Decision Support Systems (DSS), software that allow users to improve situation assessment, helping all those who must make strategic decisions. Hand in hand with the growing interest in DSS there is an increasing use of communication systems based on IT. First responders know that to face an emergency everything must be prepared and planned, also communication. In fact, DSS and voice/data transmission systems are often integrated into a single system, as proposed by the European projects FIRE IN and IN PREP, because managing information is crucial for carrying out rescue activities in the best possible way. This work describes the impact of new technologies on rescue and emergency management in Italy and Europe, highlighting the challenges associated with their use.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.041 | 0.020 |
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".