ONTOLOGICAL MODELS AND EXPERT SYSTEMS IN DECISION SUPPORT OF EMERGENCY SITUATIONS
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
During emergency response operations many decisions have to be made. Information technologies provide possibilities for new tools to support decision makers in decisions that comprise of many critical factors and that require specialized knowledge. In these tools the complexity is tackled using modelling and simulations of possible scenarios of response operations. Today, conceptual modelling in the field of information technology is oriented on the ontological approach. Ontology is a shared vocabulary and an unambiguous machine processed specification of terms together with their relationships. The ontology can have the form of a taxonomy or classification, database schema or axiomatic theory. The ontological modelling can be utilized along with expert systems for decision support. Expert systems, in contrast to other approaches such as neural networks for instance, better reflect the domain knowledge and provide justification for the decision. The aim of this paper is to describe prerequisites and design general schema for decision support in response operations during biological incidents including the applicable technology.
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.
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 | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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 it