Greenhouse Gas Emissions during Cattle Feedlot Manure Composting
Bibliographic record
Abstract
The emission of greenhouse gases (GHG) during feedlot manure composting reduces the agronomic value of the final compost and increases the greenhouse effect. A study was conducted to determine whether GHG emissions are affected by composting method. Feedlot cattle manure was composted with two aeration methods--passive (no turning) and active (turned six times). Carbon lost in the forms of CO2 and CH4 was 73.8 and 6.3 kg C Mg-1 manure for the passive aeration treatment and 168.0 and 8.1 kg C Mg-1 manure for the active treatment. The N loss in the form of N2O was 0.11 and 0.19 kg N Mg-1 manure for the passive and active treatments. Fuel consumption to turn and maintain the windrow added a further 4.4 kg C Mg-1 manure for the active aeration treatment. Since CH4 and N2O are 21 and 310 times more harmful than CO2 in their global warming effect, the total GHG emission expressed as CO2-C equivalent was 240.2 and 401.4 kg C Mg-1 manure for passive and active aeration. The lower emission associated with the passive treatment was mainly due to the incomplete decomposition of manure and a lower gas diffusion rate. In addition, turning affected N transformation and transport in the window profile, which contributed to higher N2O emissions for the active aeration treatment. Gas diffusion is an important factor controlling GHG emissions. Higher GHG concentrations in compost windrows do not necessarily mean higher production or emission rates.
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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 | 0.000 |
| 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".