Utilizing Mobile Monitoring to Predict the Spatial Distribution of Urban Air Temperature and Associations of Marginalization at a Microscale in Mississauga, Ontario
Bibliographic record
Abstract
This thesis has two main objectives: to determine if the model based on continuous collection of air temperature observations could produce better performing results than a semi-stationary collection to estimate a microscale surface of urban air temperature, and second, to investigate the spatial associations between marginalization and air temperature in Mississauga, Ontario, Canada. The first objective was achieved by sampling air temperature by mobile monitoring, then comparing the performance of models generated from continuous collection and semi-stationary using regression kriging. Results demonstrated that the model generated from the air temperature data from continuous collection performed better. The second objective was achieved by expanding our air temperature results from the first objective and using ordinary least squares (OLS) regression and simultaneous autoregressive (SAR) modelling techniques to understand the associations between marginalization and air temperature. Results demonstrated that the spatial differences in urban air temperature in Mississauga do not align with marginalization.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".