Effects of a bacterial inoculant and propionic acid on preservation of high-moisture ear corn, and on rumen fermentation, digestion and growth performance of beef cattle
Bibliographic record
Abstract
Studies of aerobic stability, digestion and growth performance were conducted with steers to determine the mode of action of a bacterial inoculant in altering the feeding value of ensiled high-moisture ear corn (HMEC); a comparison was made with propionic acid (PA) treated HMEC (10 g kg–1 fresh matter). The inoculant consisted of Lactobacillus plantarumand Enterococcus faecium, and was applied as an aqueous solution to provide 104 colony-forming units (cfu) per gram of HMEC. Inoculation of HMEC was not as effective as PA in improving aerobic stability, as assessed by changes in populations of yeasts and moulds. However, steers fed inoculated HMEC gained 11% more weight (P < 0.05) than animals fed untreated HMEC, and 9% more (P < 0.10) than those fed PA-treated material. There were no effects of treatment on food intake or digestion of organic matter (OM). At 4 h after feeding, rumen pH and molar proportions of isovalerate were greater (P < 0.05) with inoculated than untreated or PA-treated HMEC. Treatment differences in aerobic stability of HMEC did not account for the responses in growth performance. It appears that improvements in growth rate of beef cattle fed inoculated HMEC may be related to pH and/or the production of iso-acids in the rumen. Key words: Beef cattle, high-moisture ear corn, inoculant, propionic acid, growth rate, rumen fermentation
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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".