Co-composting of manure with fat, oil, and grease: Microbial fingerprinting and phytotoxicity evaluation
Bibliographic record
Abstract
Sole carbon source utilization profiles to characterize compost maturity were evaluated in reference to several other physicochemical and biological maturity indices. The results suggested that the addition of fat, oil, and grease (FOG) had a significant effect on the biological processes in the sample piles when compared to the control pile. Additionally, principal components analysis of the patterns and the levels of microbial activity indicate that microbial communities differentiate in response to FOG additions from 1 to 20 L/m3. However, between 10 and 20 L/m3, no recognizable differences were found between the control and the FOG amendment communities. Biolog data indicates a shift in the structure and function of the microbial community in compost with high FOG additions, which may be a useful indicator of high functional diversity and evenness during composting processes. Finally, the germination index (GI) of lettuce increased from 9% in the control to 100% in the FOG amended compost. However, the addition of high amounts of FOG might in turn inhibit seed germination and root growth because of the high pH and electrical conductivity (EC), and the volatilization of NH3. From the present results, 10 L/m3 was found to be the optimum FOG amendment rate for manure compost. These amendment rates are empirical and may be regarded as potential guidelines to agricultural practitioners.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".