Bibliographic record
Abstract
This project was undertaken in conjunction with the Toronto Emergency Medical Services (EMS) Communications Center to examine the degree of impact changes to their existing dispatching process would have on the time a call spends in their system, as well as the staffing levels and resources they would require. Process flow diagrams were created based on interviews, observations, and research to ensure both the current and proposed systems were properly understood before the modeling commenced. Approximately three years of real-time data were supplied by the Communications Center, and this was mathematically analyzed to determine dependencies and relationships amongst several key factors. Finally, Simul8 software was used to model both the existing and proposed systems. The existing system was modeled first to ensure the correct interpretation of data and to provide a baseline benchmark against which the proposed system could be compared. The proposed system was modeled using the same data as the existing system, with modifications based on expert opinion made where required. Mid-way through this thesis, the Communications Center pulled out of the project, rendering it impossible to complete as intended since data and key assumptions were missing. The project was completed by making reasonable assumptions. A key goal was to ensure that the models were not oversimplified, and that they were coded in such a way that they would be easy to complete once the data and assumptions were
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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.495 | 0.376 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".