Reducing Response Time of Ambulance Service by Utilizing the Knowledge of Service Location of Ambulance Drivers using Self-Organizing Map
Why this work is in the frame
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Bibliographic record
Abstract
Large numbers of people are dying because ambulances are taking too long to transport patients to hospital. This work addresses the issue of reducing response time by using knowledge of location of ambulance drivers. Kohonen's SOM was used to solve the ambulance driver-scheduling problem (ADS) problem owing to its ability to break the ADS problem into smaller and manageable parts using its unique visual approach. This approach would enable the ambulance company managers, most of who still rely on some crude non-computer based system, to visually solve the ADS problem. Numerical experiments were conducted using randomly generated data representing the ADS problem. Finally, performance of SOM was measured using grouping efficacy. This work can be used independently or it can be used as a plug-in in existing scheduling system to reduce response time.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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