Inclusion of a tannin-rich legume in the diet of beef steers reduces greenhouse gas emissions from their excreta
Bibliographic record
Abstract
Abstract The objectives of this study were to determine the emission of nitrous oxide (N2O), methane (CH4), and carbon dioxide (CO2), as well as the isotopic composition of N2O from excreta of beef steers fed ‘AU Grazer’ sericea lespedeza hay [SL; Lespedeza cuneata (Dum. Cours.) G. Don]. Fifteen Brahman × Angus crossbred steers were fed one of three experimental diets: 0, 50, or 100% inclusion of SL into ‘Tifton 85’ bermudagrass hay (Cynodon spp.). Gas sampling occurred on days 0, 1, 3, 5, 7, 14, 18, 25, and 32 after urine or feces application to static chambers for two experimental periods. Effect of the day after feces application (P < 0.001), while day × inclusion of SL interaction was observed in urine (P < 0.001) for all greenhouse gases (GHG) analyzed. Peaks of emission of all GHG in urine and feces occurred in the first days (P < 0.001), with days 3 and 5 being most depleted in 15N-N2O in feces, and days 3, 5, and 7, in urine (P < 0.001). Feeding SL to beef steers was effective in mitigating the emission of GHG from the excreta, but further research is necessary to investigate the mechanisms behind the reductions.
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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".