Wetdry composting of organic municipal solid waste: current status in Canada
Bibliographic record
Abstract
Source separation of municipal solid waste into wet and dry streams is proving to be an attractive alternative in dealing with solid waste, and in achieving provincial and national waste diversion objectives. The system provides important flexibility in the number of waste streams, collection methods, collection frequency, and waste processing. In the past few years, experience has been obtained with two-, three-, and four-stream source separation and collection, composting of the organic waste fraction, and recycling of the valuable dry waste. The systems used in Guelph, Ontario, Lunenburg, Nova Scotia, and Caledon, Ontario, are presented. Public interest and participation has been high, especially when a two-stream, mandatory system is used. Thus, the City of Guelph has reported a 98% participation rate in its two-stream system which means that the public accepted the two-stream approach. Experience has shown that, as the number of streams increase, there is a greater chance of putting waste in the wrong stream. There is a strong demand for compost at a bulk price of about $30/ t FOB at the plant. The processing cost of the three plants varied from $50/t to $80/t of waste received without allowing for credits derived from extended landfill life or reduction in environmental impact.Key words: municipal solid waste, organic, source-separation, composting.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".