Real‐Time Quantification of <i>mcr</i>A, <i>pmo</i>A for Methanogen, Methanotroph Estimations during Composting
Bibliographic record
Abstract
Composting is the controlled biological decomposition of organic matter by microorganisms during predominantly aerobic conditions. It is being increasingly adopted due to its benefits in nutrient recycling, soil reclamation, and urban land use. However, it poses an environmental concern related to its contribution to greenhouse gas production. During composting, activities of methanogenic and methanotrophic communities influence the net methane (CH4) release into the atmosphere. Using quantitative polymerase chain reaction (qPCR), this study was aimed at assessing the changes in the methyl-coenzyme M reductase (mcrA) and particulate methane monooxygenase (pmoA) copy numbers for estimation of methanogenic and methanotrophic communities, respectively. Open-windrow composting of beef cattle (Bos Taurus L.) manure with temperatures reaching > 55 degrees C was effective indegrading commensal Escherichia coli within the first week. Quantification of community DNA revealed significant differences in mcrA and pmoA copy numbers between top and middle sections. Consistent mcrA copy numbers (7.07 to 8.69 log copy number g(-1)) were detected throughout the 15-wk composting period. However, pmoA copy number varied significantly over time, with higher values during Week 0 and 1 (6.31 and 5.41 log copy number g(-1), respectively) and the lowest at Week 11 (1.6 log copy number g(-1)). Net surface CH4 emissions over the 15-wk period were correlated with higher mcrA copy number. Higher net ratio of mrA: pmoA copy numbers was observed when surface CH4 flux was high. Our results indicate that mcrA and pmoA copy numbers vary during composting and that methanogen and methanotroph populations need to be examined in conjunction with net CH4 emissions from open-windrow composting of cattle feedlot manure.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".