Molecular hydrogen generated by elemental magnesium supplementation alters rumen fermentation and microbiota in goats
Bibliographic record
Abstract
We tested the hypotheses that supplementation of a diet with elemental Mg increases ruminal dissolved H2 (dH2) in rumen fluid, which in turn alters rumen fermentation and microbial community in goats. In a randomised block design, twenty growing goats were allocated to two treatments fed the same basal diet with 1·45 % Mg(OH)2 or 0·6 % elemental Mg. After 28 d of adaptation, we collected total faeces to measure total tract digestibility, rumen contents to analyse fermentation end products and microbial groups, and measured methane (CH4) emission using respiration chambers. Ruminal Mg2+ concentration was similar in both treatments. Elemental Mg supplementation increased dH2 at 2·5 h post morning feeding (+180 %, P<0·001). Elemental Mg supplementation decreased total volatile fatty acid concentration (-8·6 %, P<0·001), the acetate:propionate ratio (-11·8 %, P<0·03) and fungal copy numbers (-63·6 %, P=0·006), and increased propionate molar percentage (+11·6 %, P<0·001), methanogen copy numbers (+47·9 %, P<0·001), dissolved CH4 (+35·6 %, P<0·001) and CH4 emissions (+11·7 %, P=0·03), compared with Mg(OH)2 supplementation. The bacterial community composition in both treatments was overall similar. Ruminal dH2 was negatively correlated with acetate molar percentage and fungal copy numbers (P<0·05), and positively correlated with propionate molar percentage and methanogen copy numbers (P<0·05). In summary, elemental Mg supplementation increased ruminal dH2 concentration, which inhibited rumen fermentation, enhanced methanogenesis and seemed to shift fermentation pathways from acetate to propionate, and altered microbiota by decreasing fungi and increasing methanogens.
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 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 | 0.001 |
| 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 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".