Bench-scale study of the biodegradation of grease trap sludge with yard trimmings or synthetic food waste via composting
Bibliographic record
Abstract
The aim of this study was to evaluate the biodegradation of lipid-rich residues (grease trap sludge) when composted under aerobic conditions, using two different substrates — yard trimmings and synthetic food waste. Addition of lipids seemed to have a very marked effect on the temperature profiles. For the high-rate phase of composting, grease trap sludge was degraded by 39%–51% for the yard trimmings experiments and 10%–27% for the synthetic food waste treatments. For the curing phase, lipids reductions were 9%–26% and 16%–45% for treatments with yard trimmings and synthetic food waste, respectively. For the active phase of composting, the volatile solids biodegradation rate coefficient for the grease trap sludge treatments was 0.009–0.033 d–1, while the average biodegradation rate coefficient for grease trap sludge alone was 0.058 d–1. The yard trimmings treatment with grease trap sludge added at 5% dry solids (ds) resulted in enhanced performance, in terms of temperature profile, rate and extent of biodegradation of solids and lipids, and reduction in wet mass and water content, when compared with the composting of yard trimmings alone. For the synthetic food waste treatments, the addition of grease trap sludge (up to 10% ds) seemed to be inhibitory of the composting process. Key words: lipid residues, compostability, biodegradation rate, biodegradation extent.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".