Estimates of air pollution mitigation with green roofs using the UFORE model
Bibliographic record
Abstract
The purpose of this study was to investigate the effect of green roofs on air pollution in urban Toronto. The researchers looked for synergistic effects in air pollution mitigation by manipulating quantities and species of trees and shrubs at grade level and grass on roofs within the study area. The effect of these vegetation manipulations were simulated using the Urban Forest Effects (UFORE) computer model developed by the USDA Forest Service Northeastern Regional Station, Syracuse, New York. Originally UFORE was developed to help forestry managers and researchers quantify urban forest structure and risks based on standard field, meteorological and pollution data. While UFORE contains four different assessment modules A - D, Module D quantifies the effect of vegetation on air contaminants such as NO₂, SO₂, CO, PM₁₀ and ozone. UFORE also provides data about hourly air contaminant removal rates and it predicts an economic externality value in USD for total air contaminant levels. Results of the study indicate that grass on roofs (green roofs) could play a significant role in air pollution mitigation in an urban neighbourhood. By extension, a 10-20% increase in the surface area dedicated to green roofs on downtown Toronto buildings would improve air quality and quality of life for citizens of Toronto.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".