Influence of dietary docosahexaenoic acid supplementation on the overall rumen microbiota of dairy cows and linkages with production parameters
Bibliographic record
Abstract
The rumen microbiota contributes to greenhouse gas emissions and has an impact on feed efficiency and ruminant product fatty acid composition. Dietary fat supplements have shown promise in reducing enteric methane production and in altering the fatty acid profiles of ruminant-derived products, yet in vivo studies on how these impact the rumen microbiota are limited. In this study, we investigated the rumen bacterial, archaeal, fungal, and ciliate protozoan communities of dairy cows fed diets supplemented with 4 levels of docosahexaenoic acid (DHA) (0, 25, 50, and 75 g·cow(-1)·day(-1)) and established linkages between microbial communities and production parameters. Supplementation with DHA significantly (P < 0.05) altered rumen bacterial and archaeal, including methanogenic archaeal, communities but had no significant (P > 0.05) effects on rumen fungal or ciliate protozoan communities. Rumen bacterial communities of cows receiving no DHA were correlated with increased saturated fatty acids (C18:0 and C11:0) in their milk. Furthermore, rumen bacterial communities of cows receiving a diet supplemented with 50 g DHA·cow(-1)·day(-1) were correlated with increases in monounsaturated fatty acids (C20:1n-9) and polyunsaturated fatty acids (C22:5n-3; C22:6n-3; C18:2 cis-9, trans-11; C22:3n-6; and C18:2n-6 trans) in their milk. The significant diet-associated changes in rumen archaeal communities observed did not result in altered enteric methane outputs in these cows.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".