PSI-14 Humic substances alter ammonia production and the microbial populations within a RUSITEC fed a mixed hay – concentrate diet.
Bibliographic record
Abstract
The aim of this study was to examine the effects of humic substances on fermentation characteristics and microbial communities using the rumen stimulation technique (RUSITEC). The experiment was conducted as a completely randomized design over a 15-d period with 3 treatments duplicated in 2 runs with 2 replicates per run. Treatments consisted of a control diet (forage:concentrate; 60:40) without humic substances or humic substances added at either 1.5 g/d or 3.0 g/d. Dry matter disappearance, pH, fermentation parameters and gas production were measured from d 8 to 15. Samples for microbial profiling were taken on d 5, 10 and 15 using the digested feed bags for solid-associated microbes (SAM) and fermenter fluid for liquid-associated microbes (LAM). The inclusion of humic substances had no effect (P ≥ 0.19) on DM disappearance, pH or the concentrations of VFA. The production of NH3 was linearly decreased (P = 0.04) with increasing levels of humic substances in the diet. There was no effect (P ≥ 0.43) of humic substances on total gas, CO2 or CH4 production. The number of OTUs was significantly reduced in the 3.0 g/d treatment compared to the control on d 10 and 15; however, the microbial community structure was largely unaffected (P > 0.05). In the SAM samples, the genera Lachnospiraceae XPB1014 group, Succiniclasticum, and Fibrobacter were reduced in the 3.0 g/d treatment and Anaeroplasma, Olsenella, and Pseudobutyrivibrio were increased at d 5, 10, or 15. Within the LAM samples, Christensenellaceae R-7 and Succiniclasticum were the most differentially abundant genera between the control and 3.0 g/d HS treatment samples (P < 0.05). This study highlights the potential use of humic substances as a natural feed additive which may play a role in nitrogen metabolism without negatively affecting the ruminal microbiota.
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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) | 0.000 | 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) | 0.001 | 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".