068 Environmental impacts from cattle consuming tannin-containing hays
Bibliographic record
Abstract
The cow–calf phase counts for approximately 80% of the total beef production system greenhouse gas emissions. There is potential for tannin-containing hays to reduce these environmental impacts. Fifteen mature beef brood cows and 9 yearling beef heifers were blocked by weight and randomly assigned to 3 groups of 5 cows or 3 heifers and fed tannin-containing (birdsfoot trefoil [BFT], sainfoin [SAN], and small burnet [SML]) or non-tannin-containing (alfalfa [ALF], cicer milkvetch [CMV], and meadow bromegrass [MB]) hays in 4 trials. Groups of cows were fed BFT, CMV, or MB in Trial 1 and ALF, SAN, or SML in Trial 3. Groups of heifers were fed ALF, MB, or SAN in Trial 2 and BFT, CMV, or SML in Trial 4. Each trial used a completely randomized block design with repeated measures during 5 d following a 14-d adjustment period. Nine cows and 9 heifers were selected for total collection of enteric methane using the SF6 methodology, feces, and urine whereas intake was measured for all animals. Methane emission from cows was lower for SML than ALF (P = 0.04) or SAN (P = 0.03; Table 068). Additionally, cows and heifers fed tannin-containing hays showed lower urine (UUN) and blood urea nitrogen (BUN) than animals fed non-tannin-containing hays (Table 068), suggesting a shift in nitrogen excretion from urine to feces. In conclusion, tannin-containing legumes consumed as hays have the potential to reduce environmental impacts of cattle fed in confinement. Means in a row with different superscripts differ (P < 0.1)
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".