Effect of a lignite-coal extract on nutrient composition and gas emissions from cattle feedlot manure
Bibliographic record
Abstract
An experiment was conducted to determine the effect of a liquid lignite coal extract (LC; pH = 3.5) on gas emissions and the chemical composition of feedlot cattle manure. Eighty steers were randomly divided into four groups, penned individually, and fed a barley grain – barley silage diet sprayed with 0 (control), 0.5, 1.0 or 2.0 L of LC per tonne of dry matter. Manure samples (mixture of excreta and wood chips) were collected 25, 53, 81, 109 and 150 d after the LC was included in the diet. Inclusion of LC in the diet reduced both the pH and dissolved NH3+ NH4+ content of manure collected on days 25 and 53. Ammonia emissions were also significantly reduced on these occasions. In addition, butyric acid content was higher and isovaleric acid content lower in manure from cattle fed LC compared with manure from control cattle. Levels of nitrate, total volatile fatty acid (VFA), acetic, isobutyric, propionic, and capric acid in manure were not altered by the inclusion of LC in the diet. Greenhouse gas emissions (CO2, CH4 and N2O) from manure were not affected by inclusion of LC in the diet. The lower manure pH, NH3 + NH4+ content and NH3 emission at early sampling dates suggest that LC could play a role in reducing gaseous ammonia N emission to the atmosphere. Further studies are needed to determine whether the reduction in dissolved NH3 + NH4+ content in manure is due to an LC-mediated change in the amount of urea produced by the animals or to the inhibition of the hydrolysis of urea in manure. Key words: Cattle (feedlot), urea (manure properties), VFA content, greenhouse gas (CH4, N2O, CO2), emission, ammonia emission
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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".