Understanding waste from a climate change perspective : municipal solid waste management in Canada
Bibliographic record
Abstract
This thesis analyses the current solid waste management situation in Canada to determine the most effective methods of managing solid waste.To arrive at best practices for sustainable waste management, the relationships between waste composition, diversion effofts, management methods and landfill characteristics were explored for 9lCanadian landfills.Municipal solid waste undergoes biological decomposition to generate landfill gas, a potent greenhouse gas that contributes to global warming.In addition I developed: l) a statistical analysis of operations and their impact on methane generation, and2) waste management guidance to reduce emissions from the solid waste sector.Landfill space is in short supply with many landfills reaching their capacity.In order to save landfill space and prevent further harm to the atmosphere, best practices in waste management have to be embraced by landfill sites across Canada.Based on the limited capacity of landfills in many regions of Canada and growing waste generation per capita a shortage of landfìll space is expected in the next twenty years, which increases the pressure for sustainable waste management practices.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 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".