A Quantitative and Qualitative Analysis of Household Organic Waste Diversion in Toronto, Canada
Bibliographic record
Abstract
Improving waste diversion is identified as one of the important strategies for municipalities to reduce carbon emissions. Toronto has developed a set of climate goals and one goal aims to reach higher residential diversion rates. However, it has not yet reached the target in the single-family homes and multi-residential buildings sectors. However, the current organic waste diversion situation indicates that there is potential to reach those targets. Considering that there will be a growing population and the low diversion rate in this sector, an analysis that focuses on improving household organic waste diversion in both sectors was conducted. Using quantitative and qualitative methods, the research evaluated the carbon emissions of Toronto's household organic waste, identified effective strategies to increase household organics diversion, explored their applicability in Toronto, and recommended six strategies to help Toronto reach its climate targets, increase organic waste diversion, and facilitate low-carbon transition.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".