Impact of urbanization on the climate of Toronto, Ontario, Canada
Bibliographic record
Abstract
Using climatic monthly means and the historical climate record, the urban heat island and lake breeze are examined for Toronto, Ontario, Canada. Data from two rural sites, Pearson airport and Vineland, Ontario show a substantial heat island peaking at 3 oC at night that has increased by at least 1 oC from 1930 to 1980. Day time heat island effect in downtown Toronto during the summer is mitigated by the presence of a lake breeze. By comparing to similar data from Montreal, Quebec, the lake breeze produces on average a 0.8 oC cooling. Downtown Toronto has the longest continuous temperature record in Canada spanning 1840 to the present. Trends of minimum and maximum temperature were examined for this period. The time period from 1840 to 1920 the diurnal temperature range either increased or stayed constant. From 1920 to present, a period that included massive urbanization for many kilometres to the east, west and north of the city, the increase in minimum temperature exceeded that of the maximum temperature. These changes in trends are related to the topography and urbanization of the Toronto area. The cooling effect of a night time downslope wind into the city core was mitigated in the latter period by the urbanization of the surrounding upslope regions of Toronto. This accounts for the changing trends and the reduction in the diurnal temperature range. This hypothesis is confirmed by comparison to surrounding rurals sites and by examining the temperature record of Montreal, Quebec. In both cities a distinct reduction of the DTR is discernible in the 1960s. This urbanization effect can account for 0.6 oC of the 1.2 oC reduction of the diurnal temperature range in Toronto since 1938.
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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 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".