Composting of municipal biosolids: effect of bulking agent particle size on operating performance
Bibliographic record
Abstract
Woodchips, prepared from wood waste obtained from a pallet manufacturer, were used to determine the effect of bulking agent particle size on compost pile performance. The three treatments investigated used coarse, medium, or fine woodchips. Characteristic particle sizes were 40, 13, and 5.2 mm for the coarse, medium, and fine material, respectively. All recipes were made using one part biosolids to 2.5 parts woodchips (v:v) at a target moisture content of 60%. Finer woodchips resulted in thermophilic temperature values being reached sooner, being sustained for a longer time (>95 d), and recovering more quickly after rainfall events. Finer woodchips also resulted in lower moisture loss over the experimental period. Based on the experimental observations, it was assumed decreased tortuosity in the coarse woodchip material led to higher ventilation rates as compared to the finer material. Further experimental work is required to confirm this. The authors' recommend operators characterize feedstock bulking agent particle size distribution, particularly at facilities purchasing bulking agents for their operations. Key words: compost, municipal biosolids, woodchips, particle size, bulking agent, temperature.
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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 | 0.000 | 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) | 0.001 | 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".