Gas emissions during cattle manure composting and stockpiling
Bibliographic record
Abstract
Abstract Manure composting is a common management practice for cattle feedlots, but gaseous emissions from composting are poorly understood. The objective of this study was to quantify ammonia (NH 3 ), nitrous oxide (N 2 O), carbon dioxide (CO 2 ), and methane (CH 4 ) emissions from windrow composting (turning) and static stockpiling (nonturning) of manure at a commercial feedlot in Australia. An inverse‐dispersion technique using an open‐path Fourier transform infrared (OP–FTIR) spectrometer gas sensor was deployed to measure emissions of NH 3 , N 2 O, CO 2 , and CH 4 over a 165‐d study period, and 29 and 15% of the total data intervals were actually used to calculate the fluxes for the windrow and stockpile, respectively. The nitrogen (N) lost as NH 3 and N 2 O emissions represented 26.4 and 3.8% of the initial N in windrow, and 5.3 and 0.8% of that in the stockpile, respectively. The carbon (C) lost as CO 2 and CH 4 emissions represented 44 and 0.3% of the initial C in windrow, and 54.8 and 0.7% of that in the stockpile, respectively. Total greenhouse gas (GHG) emissions from the manure windrow were 2.7 times higher than those of the stockpiled manure. This work highlights the value that could be accrued if one could reduce emissions of NH 3 –N and N 2 O‐N from composting, which would retain manure N content while reducing GHG emissions.
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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) | 0.000 | 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".