Bibliographic record
Abstract
The urban heat island (UHI) effect is known to manifest unevenly throughout the built environment, often seen disproportionately affecting marginalized communities across North American cities. These heat inequities arise from the uneven socio-environmental conditions that structure life within urban spatial configurations. Heat inequity in Toronto is seen through positive correlations between summertime land surface temperature (LST) anomalies and the various Ontario Marginalization Index (ON-Marg) dimensions. In Toronto, some heat inequities are only visible at the district level yet remain invisible when considered at the municipal scale. Urban greenspace has been shown to have a negative relationship to a neighbourhood’s LST. Greenspace in Toronto, as measured using the solar induced chlorophyll fluorescence (SIF) product and the City of Toronto’s tree canopy coverage dataset, is shown to have similar negative relationships with LST on the census tract level. Therefore, additions of greenspace in Toronto can help mitigate existing heat inequities. Additional greenspace can lower neighbourhood-level heat in areas that experience elevated temperatures and low access to cooling amenities. From this study, its recommended that further action be taken by the City of Toronto to mitigate existing urban heat inequities, to which the increased equitable distribution of greenspace and changes to the Toronto heat relief network can be major drivers of positive societal change.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".