Food Waste Treatments and the Impact of Composting on Carbon Footprint in Canada
Bibliographic record
Abstract
Forty percent of the food generated in Canada is wasted, making it the most significant component of municipal solid waste. Food waste characteristics, such as high moisture and oil content, and variable composition, make it difficult to manage with conventional waste treatment methods. Part of food waste is disposed of in landfills, generating greenhouse gases and significantly increasing the carbon footprint. Various treatment methods such as composting and anaerobic digestion have been employed to treat and manage the remaining waste efficiently. This study provides an overview of the impact of composting as a food waste treatment method in Canada and paves way for the research of the usefulness of composting in addition to other food waste treatment methods such as anaerobic digestion. Average composting data for Canada was used to determine the change in the carbon footprint by the diversion of food waste using CCaLC2 software. It was determined that the overall carbon footprint of 1.38 and 1.33 mega-tons of CO2 was reduced from the composting of food waste in the years 2014 and 2016, which were approximately 18% and 20% of the total footprint of Canada municipal solid waste, respectively. The carbon footprint data collected herein were compared to the data from England, Sweden, and the USA to reveal the high effectiveness of composting in Canada.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".