Effects of citric acid and heat-treated soybean meal on rumen fermentation characteristics, methane emissions, and microbiota: an in vitro study
Bibliographic record
Abstract
This study aimed to assess the impact of citric acid (CA) and heat-treated soybean meal (SBM) on rumen fermentation characteristics, methane production, and microbiota through an in vitro experiment. Untreated SBM, heat-treated SBM (HSBM), CA-treated SBM (CSBM), and SBM treated with a combination of heat and CA (HCSBM). Parameters assessed in in vitro were gas production, methane emissions, dry matter degradability (IVDMD), crude protein degradability (IVCPD), ammonia nitrogen (NH3-N), microbial crude protein (MCP), volatile fatty acids (VFA), pH, and microbiota composition. The HCSBM exhibited the lowest gas production and theoretical maximum gas production (p < 0.01). Methane production (%) was significantly reduced in both CSBM and HCSBM (p < 0.01), with the lowest methane emissions (mL/g dry matter, DM) observed in HCSBM (p < 0.01). The IVCPD was significantly reduced in both the HSBM and HCSBM groups (p < 0.01). HCSBM had the lowest NH3-N and MCP concentrations (p < 0.01). Total VFA production was the lowest in HCSBM (p < 0.01), with a higher proportion of acetate and lower proportions of propionate (p < 0.01). HCSBM reduced the enrichment of Thermoplasmatota compared to HSBM (p < 0.05) and decreased the enrichment of the coenzyme M biosynthesis pathway in the microbial functional profiles compared to SBM and CSBM (p < 0.05). Additionally, an increase in fiber-degrading bacteria, particularly Fibrobacterota, was observed in HCSBM (p < 0.05). These findings suggest that the HCSBM may effectively reduce ruminal protein degradation and methane emissions. Further in vivo studies are necessary to validate these results and assess their practical application in ruminant nutrition.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".