COMPOSTING DUCK EXCRETA ENRICHED WOOD SHAVINGS: C AND N TRANSFORMATIONS AND BACTERIAL PATHOGEN REDUCTIONS
Bibliographic record
Abstract
Composting of agricultural and domestic wastes is used increasingly to reduce weight, volume, and odor; destroy animal and plant pathogens; and improve the quality of end-products to be used as soil amendments and growth substrates. The objective of this study was to investigate the transformation of C and N and the survival of bacterial populations and pathogenic bacteria during in-vessel composting of duck excreta enriched wood shavings. Two feedstocks, collected on different dates, were composted (C1 and C2) in an enclosed hall system equipped with an electromechanical turner. Temperature was continuously recorded, whereas moisture content and bacterial counts were determined twice a week. Data showed that, although the N content of C2 was only half of that of C1, both materials were fully biostabilized at the end of the composting period as indicated by extractable lipid ratios. In the compost with the low C/N ratio (C1), all bacterial populations were eliminated, whereas fecal streptococci, total coliforms, and gram-negative bacteria were still present in C2 at the end of the composting period. Our results emphasize that the composting of manures and other organic wastes needs to be properly managed to stabilize C and N and to eliminate or reduce bacterial populations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".