Increases in microbial nitrogen production and efficiency in vitro with three inhibitors of ruminal methanogenesis
Bibliographic record
Abstract
It was hypothesized that the addition of crotonic acid or 3-butenoic acid would relieve constraints in digestibility observed when methane formation is inhibited by lumazine, propynoic acid, or ethyl 2-butynoate. In six incubations, one of the three methanogenesis inhibitors, at three different concentrations, was combined with either crotonic acid or 3-butenoic acid at two different concentrations. A mixture of buffer and ruminal fluid (4:1) was incubated with grass hay in Erlenmeyer flasks for 72 h. Initial concentrations were 0, 0.6, and 1.2 mmol/L for lumazine; 0, 2, and 4 mmol/L for propynoic acid; and 0, 4, and 8 mmol/L for ethyl 2-butynoate. 15Nitrogen (N) incorporation was used as a microbial marker. All three methanogenesis inhibitors decreased proteolysis. Propynoic acid and ethyl 2-butynoate at 8 mmol/L also decreased the digestibility of organic matter and neutral detergent fibre. However, all three inhibitors of methanogenesis increased the production of microbial N through an improvement of synthetic efficiency. Crotonic acid and 3-butenoic acid were generally ineffective in compensating digestibility decreases caused by the inhibitors of methanogenesis. It is of interest to elucidate the mechanisms by which these compounds increased the efficiency of microbial N production. Lumazine and the addition of low levels of ethyl 2-butynoate could potentially benefit animal production by lowering methane emissions, decreasing ruminal proteolysis, and increasing microbial N production without affecting organic matter digestibility.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".