Anthropogenic Drivers of Neighbourhood-Level Carbon Dioxide Emissions in Montreal
Bibliographic record
Abstract
The urban carbon cycle describes the relationship between urban form and carbon dioxide emissions from human activity in cities (Pataki et al. 2006). However, a mere handful of studies explore how the built environment affects carbon dioxide emissions both directly, through reduced carbon sequestration capacities, and indirectly, through population travel behaviour (Grimmond et al.,1987; Ewing et al., 2008;). This thesis takes advantage of a unique opportunity to compare high-quality neighbourhood-level CO2 data to travel behaviour along an urban-suburban-exurban gradient in Montreal. It interprets CO2 observations collected in the scope of the Environmental Prediction in Canadian Cities Project (EPiCC) in light of urban travel trends computed from the Agence Métropolitaine de Transport‘s 2003 Origin-Destination Survey. Factor analysis is used to group census tracts sharing similar urban form and demographic composition such that urban, suburban and exurban travel behaviour can be compared to CO2 concentrations and fluxes from these different neighbourhood types. Although mature suburban and exurban neighbourhoods were found to be effective daytime carbon sinks in the summer, their inhabitants use more carbon-intensive modes of transportation. [...]
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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".