Green alleys in Quebec provide variable biodiversity support and ecosystem services
Bibliographic record
Abstract
Abstract Green infrastructure is increasing in popularity in cities globally because of its potential to improve urban sustainability and resident quality of life. In this paper, we studied green alleys in two Quebec cities, one with a resident-led green alley program and one with a municipally led program. Green alleys are conceptualized and promoted as green infrastructure that provide many benefits for urban residents. Using mixed social and ecological methods, we assessed 53 green alleys’ capacity to support biodiversity and provide ecosystem services, alongside 23 grey alleys and 76 streets. We interviewed residents to select the ecosystem services that were most relevant to people living around green alleys and then measured indicators of ecosystem service capacity with traditional ecological techniques, harnessing an interdisciplinary approach to ecosystem service assessment. Green alleys provided more biodiversity support than grey alleys and adjacent street segments but did not consistently increase the capacity for ecosystem services. Vegetative complexity and proportion of native tree species are both higher in green alleys than traditional grey alleys and adjacent streets. The proportion of flowering trees was one indicator of ecosystem services that was consistently higher in green alleys. Resident-led vs municipally led creation and management of green alleys resulted in different results, where resident-led alleys were more able to target the needs of residents but resulted in high levels of variation in both support for biodiversity and ecosystem services. We recommend ongoing funding paired with technical expert support to increase the impact of green alleys.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".