Estimating the urban metabolism of Canadian cities: Greater Toronto Area case study
Bibliographic record
Abstract
An urban metabolism analysis is a means of quantifying the overall fluxes of energy, water, material, and wastes into and out of an urban region. Analysis of urban metabolism can provide important information about energy efficiency, material cycling, waste management, and infrastructure in urban systems. This paper presents the first urban metabolism of a Canadian urban region, and possibly the first for a North American city. It also makes a first attempt at comparing the urban metabolisms of a few cities worldwide. The most noticeable feature of the Greater Toronto Area metabolism is that inputs have generally increased at higher rates than outputs over the study years (1987 and 1999). The inputs of water and electricity have increased marginally less than the rate of population growth (25.6%), and estimated inputs for food and gasoline have increased by marginally greater percentages than the population. With the exception of CO2 emissions, the measured output parameters are growing slower than the population; residential solid wastes and wastewater loadings have actually decreased in absolute terms over the 12 year period from 1987 to 1999.Key words: urban metabolism, urban sustainability, Canadian cities, materials, food, water and energy consumption, waste outputs.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| 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) | 0.002 | 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".