Emission of Carbon Monoxide During Composting of Municipal Solid Waste
Bibliographic record
Abstract
Elevated concentrations of carbon monoxide (CO) have been observed at the enclosed municipal waste composting facility (ECF) in Edmonton, Canada. Elevated concentrations of CO in an enclosed facility pose a potential health risk to workers. The objectives in this study were to: (1) assess temporal and spatial variability of CO emissions from the composting bays in the ECF using Fourier Transform Infrared (FTIR) spectroscopy; and (2) identify any correlations between the CO emission rate and the physicochemical properties of the compost through bench-scale incubation experiments. Repeated gas measurements were taken above and within the compost bed in the ECF using a probe connected to an FTIR gas analyzer, which continuously collected concentration data. These preliminary field measurements showed maximum CO concentrations of 112 μL−1 within the compost. Autoclaved and non-sterilized compost samples from the ECF were incubated under aerobic and hypoxic conditions, and gas emissions were quantified using gas chromatography (GC). These trials showed a positive correlation between CO emission rate and incubation temperature for all samples, indicating a physico-chemical source of CO generation. Lower concentrations of CO were observed in the non-sterilized compost under both aerobic and anaerobic conditions, presumably due to the microbial metabolism of CO.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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 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".