Impacts of liquid milk permeate supplementation on rumen microbial fermentation and community structure in an in vitro semi-continuous culture system
Bibliographic record
Abstract
We investigated the potential of liquid milk permeate as a microbial energy source, in an in vitro semi-continuous culture system, and its impact on rumen microbial communities. Sixteen fermenters in a 2 × 4 randomized complete block design, over two 14-day periods to assess the impact of 50 mL/day of various liquids (C: control, no liquid; W: water; PW: 50:50 water:permeate; P: permeate) under two buffering conditions (N: normal; S: subacute ruminal acidosis). The V3–V4 region of the 16S rRNA gene was sequenced using Illumina MiSeq. Data analysis in R examined the effects of treatments on microbial taxa, pH, and short chain fatty acids (SCFA). Water addition did not significantly affect pH, gas production, or SCFA proportions ( p ≥ 0.05) but reduced NH 3 concentration in both buffer conditions ( p ≤ 0.001). Permeate lowered pH and NH 3 in both buffering conditions ( p ≤ 0.001) and altered microbial composition without negatively impacting bacterial diversity. These findings suggest that water can be added to a semi-continuous in vitro system without disrupting microbial balance, enhancing model accuracy. Permeate can be a source of energy in liquid form but it must be balanced with adequate protein to avoid reductions in NH 3 .
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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 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.000 | 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".