The quantity of food waste in the garbage stream of southern Ontario, Canada households
Bibliographic record
Abstract
There is little consensus on the amount of worldwide food waste generation because many current estimates are indirect and link back to the same limited primary datasets, with much of the data originating from fieldwork undertaken in the 1970s and 1980s. Direct measurement of waste streams, through waste composition studies, can be used to develop accurate estimates of food waste disposal. In Ontario, Canada, municipalities that undertake household waste composition studies all use a common direct measurement methodology that includes a broad range of waste categories, including food waste. The purpose of this research was to estimate the quantity of food waste disposed, in the garbage stream, by households in southern Ontario, Canada, and determine if this common methodology could be expanded and serve as the basis of a standardized and rigorous household food waste measurement methodology. Household waste composition study data (2012-2015), including a single "food waste" category, were gathered from 9 Ontario municipalities, aggregated and analyzed to develop estimates of food waste in the garbage stream. On average, households disposed 2.40 kg/week of food waste in the garbage, which comprised 35.4% of this waste stream. This does not include any food waste otherwise disposed (e.g., sink) or recycled (e.g., composted). Urban households disposed significantly greater amounts of food waste compared to rural households in the spring (p = 0.01) and summer (p = 0.02). Households with access to a green bin program disposed significantly less food waste than those with no access to a green bin program in the spring (p = 0.03) and summer (p<0.01). The common methodology used to develop these estimates shows promise as the basis of a household food waste measurement methodology. This future methodology would include dividing food waste into avoidable and unavoidable food waste categories, as well as adding subcategories (e.g., avoidable fruits and vegetables).
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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 | 0.001 |
| 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".