1 DEVELOPMENT PATTERNS IN CANADA’S LARGEST URBAN AGGLOMERATION: FOUR DECADES OF EVOLUTION
Bibliographic record
Abstract
The city of Toronto, Ontario and its surrounding regions constitute the largest urban agglomeration in Canada and the fifth largest in North America. Urban development within this area is an impor-tant planning and environmental issue. Landsat images from 1972 to 2004 (a total of 10 scenes covering a period of 32 years) were used in this research that cover the majority of the contiguous urban area. A series of change detection experiments were performed that compared methodolo-gies and techniques. The results greatly improved classification accuracy, particularly for Landsat Multispectral Scanner (MSS) data. The distribution of urban growth becomes apparent when divided by municipality. The City of To-ronto is the largest municipality, and it accounted for 16.07 % of total urban change. Mississauga was the largest contributor, accounting for 21.29%, although its municipal area is only about half that of Toronto. Development prior to 1972 within the Toronto municipal boundaries helps in pro-viding an explanation for this finding. The next largest contributors were Brampton (14.91%), Vaughan (13.62%), and Markham (10.02%). Ajax and Pickering accounted for the smallest propor-tion of the total change, at 3.37 % and 3.93 % respectively although this may have been influenced by missing data (due to WRS-2 scene divisions) in the northeast corner of some of the Landsat5 and Landsat7 Path 18 Row 30 scenes. Overall, a yearly average of 14.1 km2 of new development was observed.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".