Bibliographic record
Abstract
This paper studies the spatial distribution of retail gasoline prices in the City of Mississauga, part of the Greater Toronto Area (GTA), over a one-month period in spring 2022. Over the past few years, the retail gasoline market has been affected by the COVID-19 pandemic, supply chain shortages, inflationary pressures, and of course the Russia-Ukraine war. While these are broad pressures that influence gas prices at a macro level, this research considers whether there is local variability. This study will utilize retail gasoline price data collected from a third-party crowdsourced website (Gas Buddy) three times a day (morning, afternoon and evening periods) for a total period of 30 days. Data was analyzed at the trade-area scale for the 121 gas stations in Mississauga using Emerging Hotspot Analysis. Overall, there was evidence of spatial and temporal patterns in Mississauga. The south side of Mississauga close to Oakville and the lakeshore areas were consistently classified as hotspots whereas the area around the Streetsville and Pearson airport were consistently classified as cold spots. Demographic data suggests that higher socioeconomic status, particularly education and income, consistently results in hotspots while areas with lower socioeconomic status as well as having a higher unemployment rate consistently results in cold spots.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".