Managing tools for the composting of urban food waste
Bibliographic record
Abstract
Quantification and characterization of urban food waste (UFW) and bulking agents is essential to obtain the best compost recipe for an accelerated process, minimal odor emissions and limited leachate production. For downtown Montreal and from May to August 2004, the study examined monthly variation in carbon (C), nitrogen (TKN), C/N ratios and dry matter (DM) of food waste (FW). The FW was collected from residences, a restaurant and a community kitchen. Locally available bulking agents were also analyzed to compare characteristics for the best composting process. Furthermore, trials were conducted using an urban composting unit prototype to compare the performance of various recipes. In each trial, temperature and pH were recorded every day and other day, respectively.\nThe C, TKN, C/N ratio and DM of the UFW were found to vary from 49.3% to 47.9%, 1.7% to 2.7%, 29.1 to 17.9 and 13.7% to 10.3% from May to August respectively. These variations resulted from more fresh vegetables and fruits being consumed by the end of the summer. The C/N ratios of chopped wheat straw (CWS) and chopped hay (CH) were found 100 and 58 with DM of 89% and 91%, respectively. From trial experiments, the best ratios (weight basis) of UFW to CWS and CH were found to be 8.9:1 and 8.6:1, respectively. Thus, the composting recipe m ust be adjusted regularly to correct for variations in UFW characteristics.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".