The 3-30-300 Indicator, a Tool for Assessing Geographical Access and Inequalities Associated with Green Spaces: Demonstration Project for the Island of Montreal, Canada
Bibliographic record
Abstract
(1) Background: this study examines the effectiveness of the 3-30-300 indicator as a tool for assessing the accessibility of green spaces in the island of Montreal, Canada. The 3-30-300 framework defines three standards for urban vegetation: every resident should be able to see three trees from their home, live in a neighborhood that has 30% canopy cover, and have access to a park or green area within 300 meters. This study measures the distribution of these criteria across Montreal's neighborhoods using geospatial analysis and examines disparities linked to so-cio-economic factors. (2) Methods include spatial analysis using metrics like Getis-Ord G and Lee's L to identify patterns and correlations with socio-economic vulnerabilities. (3) The results show that there are inequalities in the distribution of green spaces: only a limited neighborhoods meet all the 3-30-300 criteria, while several outlying areas lag. (4) Conclusions: such results clearly show the need to have specific urban greening programs, particularly in neighborhoods that face so-cioeconomic challenges, to reduce inequalities in accessibility to green infrastructure for improved public health and urban resilience.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".