A Geographical Approach to Identifying Vegetation-Related Environmental Equity in Canadian Cities
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The research in this paper addresses human — environment interactions in Canadian cities by examining the spatial distribution of vegetation in relation to various socioeconomic indicators. Specifically, intercity and intracity comparisons are evaluated using correlation analysis and geographically weighted regression (GWR). Vegetation abundance estimates derived from spectral mixture analysis of Landsat imagery are compared with Canadian census data for the cities of Montreal, Toronto, and Vancouver to quantify vegetation-related environmental equity in Canada's largest urban centres. Results exhibit strong and consistent correlations between median family income and vegetation fraction for Montreal ( r = 0.473), Toronto ( r = 0.467), and Vancouver ( r = 0.456). Furthermore, examining the GWR results suggests that employing an adaptive bandwidth kernel technique with a manual selection of ten neighbours for each observation provides a greater range and higher median values for local regression estimates (Montreal: 0.69; Toronto: 0.74; Vancouver: 0.73) as compared with the Akaike information criterion-selection method. Finally, we discuss the potential application of the presented analysis techniques for urban planning and community-development initiatives, specifically associated with managing vegetation-related environmental equity at various scales. Possible applications of these techniques for urban planning purposes are discussed, and key methodological considerations for performing such an analysis are highlighted.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it