Bibliographic record
Abstract
This research explores how service area delineation techniques can be applied to estimate the population serviced by municipally operated indoor swimming pools in 2020 and 2025 in the City of Toronto, Ontario, Canada. Municipalities face financial costs and the need to serve a growing population as challenges to providing access to pool facilities across Toronto. Comparing deterministic and probabilistic service area delineation techniques and analyzing service area demographics are steps to identify if these pool facilities are accessible across the city. Deterministic service areas using radius buffers and drive-distance buffers were created with a 2km travel cost, while the Huff Model used census tracts with a probability of 0.20 to determine its service area. The results of this study showed that deterministic service area techniques create larger service area populations than the probabilistic Huff Model, and that the demographic composition of the populations have slightly higher proportions of lower-income households, and lower proportions of visible minorities. The addition of six new indoor pool facilities planned for completion by 2025 will service both highly populated areas and lower populated areas with differing demographics.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".