Characterisation of food waste and bulking agents for composting
Bibliographic record
Abstract
The characterization of food waste (FW) and locally available bulking agents (BA) are a prerequisite to optimizing compost recipes.\nThis study measured the variation in FW characteristics (pH, dry matter (DM), carbon (C), wet bulk density and Total Kjeldahl Nitrogen\n(TKN)) produced by a restaurant and a community kitchen in downtown Montreal, Canada from May to August 2004. The project\nalso measured the mass of FW produced by another restaurant and a group of 2048 households, from June to August 2004. Locally\navailable BA (hay, straw, pine wood shavings, cardboard, left over cattle feed and wheat residue pellets) were also characterized to formulate composting recipes based on the FW characteristics observed during a period representative of winter and summer conditions.\nResidential and restaurant FW characteristics varied significantly over the summer months, although the mass produced remained constant\nat 0.61 and 0.56 kg capita1 day1, respectively. In addition, the number of customers served by the restaurant increased by nearly\n50% from June to August. The BA with the highest moisture adsorption capacity was found to be the wheat residue pellets, followed by\nchopped straw. Wheat residue pellets, chopped hay and left over cattle feed all presented a balanced C/N ratio. Wheat residue pellets and wheat straw, chopped hay and cardboard demonstrated neutral pH values. Based on the variable FW characteristics and monthly production rates, the formulation of recipes indicates that compost facilities must be flexible enough to handle seasonal variations of as\nmuch as 50% by volume.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".