A spatio-temporal analysis of the relationship between housing renovation, socioeconomic status, and urban forest ecosystems
Bibliographic record
Abstract
Urban forest ecosystems are increasingly recognized as necessary components of a city's overall sustainability. The number of municipal governments planning and implementing urban forest management programs is rising, as the benefits of urban forest ecosystems are becoming common knowledge. However, the urban forest is an exceedingly complex and vulnerable social–ecological system that presents a wide array of management challenges. One area of concern that is understudied and worthy of investigation is the effects of housing renovation activities and neighborhood revitalization on the urban forest. The purpose of this study is to investigate the possibility of renovation activity as a significant source of disturbance in urban forest ecosystems. We conducted ordinary least squares regression and geographically weighted regression analyses using canopy cover, building permit data, and socioeconomic variables in Toronto, Canada. We then conducted a parcel-level assessment of tree mortality using ortho-imagery from 2003 and 2014 and government open data describing 16 years of renovation activity. Findings suggest that renovation activity, as indicated by building permit abundance, is a possible cause of tree mortality and subsequently a source of urban forest disturbance. Our findings also suggest that the relationship between renovation activity and canopy cover is highly complex, and is likely influenced by residential tree planting rates, land use mix, and different trajectories of urban change.
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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 | 0.001 | 0.002 |
| 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) | 0.001 | 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".