Quantification of organic acids in ruminal in vitro batch culture fermentation supplemented with fumarate using a herb mix as a substrate
Bibliographic record
Abstract
Two 24 h in vitro batch culture experiments were conducted to investigate the effects of fumarate addition (10 mmol L−1) on the ruminal fermentation parameters of selected medicinal herbs, and the effects of different doses of fumarate (0, 10, or 30 mmol L−1) on ruminal metabolism of organic acids when a high-concentrate diet (meadow hay and barley grain, 400/600, w/w) was supplemented with a mix of medicinal herbs (Artemisia absinthium L., Melissa officinalis L., Malva sylvestris L., Matricaria chamomilla L., Plantago lanceolata L., Foeniculum vulgare Mill., and Althaea officinalis L.). Depending on the concentration, fumarate treatment decreased methane production (by 10–11%) and increased propionate proportions (by 5–13%) with high-concentrate diets. The organic acid (fumaric, succinic, malic, and lactic acid) concentrations in the batch culture were measured at intervals of 0, 4, 6, 12, and 24 h. The time and organic acid concentrations with 10 mmol L−1 fumarate were well correlated (R 2 = 0.846). The amount of succinate was accumulated and metabolized more slowly than that of fumarate (>24 h). The addition of fumarate and a herb mix could positively influence in vitro ruminal fermentation parameters of high-concentrate diets by increasing the levels of propionate and succinate as well as the pH, and by decreasing of methane emissions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".