Gas emissions during cattle manure composting and stockpiling
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it