Effects of California chaparral plants on <i>in vitro</i> ruminal fermentation of forage and concentrate diet
Bibliographic record
Abstract
BACKGROUND: The combustible nature of chaparral plants has been attributed to the presence of secondary compounds such as phenolic acids, flavonoids and essential oils, among others. However, the implication of the antimicrobial properties of secondary compounds of chaparral in modulating rumen microbial metabolism has not been determined. The effects of 11 chaparral plants on rumen microbial fermentation were assessed in an in vitro batch culture fermentation fed a barley silage:barley grain-based low concentrate (LC) and high concentrate (HC) diets. RESULTS: With LC, gas production [g dry matter (DM) incubated] and DM disappearance (DMD) were unaffected by Adenostoma fasciculatum, Ceanothus cuneatus, Baccharis pilularis or Eriodictyon californicum, but all plants except C. cuneatus decreased (P < 0.05) CH(4) production. With HC diet, all species except A. fasciculatum decreased (P < 0.01) total gas produced. Total volatile fatty acids (VFA) produced, molar proportions of acetate, propionate and butyrate, and the acetate:propionate (A:P) ratio were unaffected by chaparral addition to both HC and LC diets. Increased concentrations of A. fasciculatum and E. californicum decreased (linear; P < 0.05) fermentability and productions of CH(4) and NH(3) -N. CONCLUSION: Among all chaparral species investigated A. fasciculatum and E. californicum added as 10% of LC diet showed some potential to exert beneficial effects on rumen microbial 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 | 0.000 |
| 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".