150 Effect of Ergot Alkaloids and a Mycotoxin Deactivating Product on in vitro Rumen Fermentation Using the Rumen Simulation Technique (RUSITEC)
Bibliographic record
Abstract
Abstract A rumen simulation technique (RUSITEC) investigated effects of ergot alkaloids (E) and a mycotoxin deactivating product (Biomin® AA; B) on nutrient digestion, rumen fermentation, total gas and methane emissions. Fermenters received a finishing diet of 90:10 concentrate:barley silage. Using a randomized complete block design, treatments were assigned (4 vessels/treatment) within two RUSITECs in a 2 × 2 factorial. Treatments included: 1) control diet, 2) control + 1 g B, 3) control + 20 ppm E, and 4) control + 20 ppm E + 1 g B. The study had a 14-d experimental period, with 7-d adaptation and 7-d sample collection. Data were analyzed in SAS using PROC mixed including fixed effects of E, B, and E×B interaction. Random effects included RUSITEC apparatus and cow rumen inoculum (n = 4). Ergot decreased dry matter disappearance (DMD) (P < 0.05; 87.9 vs. 87.2%) and organic matter disappearance (OMD) (P < 0.05; 88.8 vs. 88.4%). Adding B increased OMD (P < 0.05; 88.3 vs. 88.9%) and neutral detergent fiber disappearance (NDFD) (P < 0.01); however, an E×B interaction was observed for NDFD (P < 0.01) with B promoting greater increases with E. Ergot decreased acetate proportions (P < 0.01) and increased isovalerate (P < 0.05). Consequently, acetate:propionate was reduced (P < 0.05) with E. Inclusion of B increased total volatile fatty acids (P < 0.01), and proportions of acetate (P < 0.05) and propionate (P < 0.05), and decreased valerate (P < 0.01), isovalerate (P < 0.01), and caproate (P < 0.01). Treatments did not affect (P ≥ 0.17) ammonia-N, total gas, or methane production (mg/d or mg/g of OM fermented). In conclusion, E reduced OMD and acetate production, but these responses were reversed by the addition of B.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".