A data exchange solution for emergency response systems based on the EDXL-RESCUER ontology
Bibliographic record
Abstract
Handling an emergency requires the coordination and cooperation of several people and systems from various agencies and organisations, including the government and society in general. A wide range of heterogeneous data is managed by different stakeholders, thus demanding solutions to support integration issues such as interoperability and ambiguity. In a previous work, we proposed an ontology for Emergency Response Systems, called EDXL-RESCUER, in the scope of the RESCUER project. In this paper, we present the usage of this ontology in a data exchange solution (DILS: Data Integration with Legacy Systems), which aims to provide semantic interoperability between Emergency Response Systems, and the evolution of EDXL-RESCUER. To evaluate the proposed solution, EDXL-RESCUER & DILS, we performed two studies: (i) simulations using two real data sources, the Police Reports from Bahia Public Safety and Security Department, Brazil; and the Canadian Disaster Database; (ii) an emergency simulation at an Industrial Park in Bahia, Brazil. The results demonstrate the potential use of the EDXL-RESCUER as a common vocabulary to support semantic interoperability between emergency response systems. We also verified the feasibility of using the DILS solution in emergency management scenarios. Besides EDXL-RESCUER & DILS, a mapping and integration of concepts related to data exchange are presented.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".