Tornado mitigation network analysis and simulation
Bibliographic record
Abstract
Purpose This paper aims to apply network modelling and simulation methods to critically analyse the tornado detection, warning and communication system in the Canadian Prairies. Design/methodology/approach The simulation results of the developed network illustrate the role of collaborating partners and provide a probabilistic representation of the overall time from tornado detection to warning issuance. Furthermore, the total time from the warning issuance to when evacuation is complete is analysed by combining the time distribution of the network and the evacuation time distribution, which is developed based on survey data. Findings A set of recommendations are offered as guidelines for consideration and possible adoption by collaborating partners who are involved at different stages of the detection, warning, communication and evacuation process. Practical implications The research contributes to a deeper understanding of the pre-disaster phase of tornadoes by providing an overall analysis that spans different areas under the general umbrella of disaster mitigation. Social implications This research paper helps the community to work together in developing mitigation measures to enhance social values and benefits. Originality/value This paper shows how activity network modelling and simulation methods, which are normally applied in construction management, can be used to analyse the overall process from tornado detection to the warning issuance.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".