Short Communication: Effect of Tannic Acid on Composition and Ruminal Degradability of Bermudagrass and Alfalfa Silages
Bibliographic record
Abstract
We measured the effects of the addition of tannic acid on chemical composition and crude protein (CP) ruminal degradability of bermudagrass, cv. coast cross (Cynodon dactylon L.) and alfalfa (Medicago sativa L.) silages with nylon bags incubated in the rumen of two fistulated lactating cows. Silage pH was greater for alfalfa than for bermudagrass. The addition of tannic acid had no effect on silage pH. Ammonia N was greater in alfalfa than in bermudagrass silage. Treated and control silages had similar ammonia N concentrations. The percentage of CP that was solubilized at time 0 was lower for alfalfa than for bermudagrass silage. The addition of tannic acid decreased the portion of CP solubilized at time 0 and increased the potentially degradable fraction of CP. The potentially degradable fraction of CP was greater for alfalfa than for bermudagrass. There was an interaction of species and treatment; the disappearance rate of CP increased with tannic acid treatment of alfalfa but decreased with acid treatment of bermudagrass. The effective degradability of CP was similar for control and treated alfalfa but lower for treated than for control bermudagrass. Tannic acid treatment is effective in decreasing the rapidly soluble fraction of alfalfa and bermudagrass silages, which could be beneficial to the animal because it would decrease the excess of N in the rumen after feeding. Tannic acid treatment decreased CP degradability of bermudagrass silage but had no effect on alfalfa silage, suggesting that tannic acid concentration required to effectively decrease CP degradability differs among forages.
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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".