Facility location in cities : the optimal location of emergency units within cities
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
Over the past four decades, there has been an increasing interest in the problem of effective facility location in many societies. The question arises as to how many schools, hospitals, ambulances, warehouses, fire stations, emergency centers are needed and their respective locations to achieve a prescribed level of service. This book is concerned with the problem of locating emergency facilities efficiently and effectively in cities. We develop three new heuristic methods with the objective of increasing accessibility to these facilities and thus reducing their response time. This book details the development, implementation, and testing of the three new heuristics in addition to applying our best heuristic to the location of ambulance stations in the Perth metropolitan area in Western Australia. Furthermore, we also developed an effective method for locating new facilities among old facilities. This book is well suited for students, lecturers, researchers, and anyone who is interested in the location and operation of emergency facilities in some major cities such as New York, Austin, Los Angeles, Denver, Vancouver, Bangkok, Taipei, Santo Domingo and Belfast.
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 | 0.000 | 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 | 0.000 | 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