Pretreatment of crop residues by ammonia fiber expansion (AFEX) alters the temporal colonization of feed in the rumen by rumen microbes
Bibliographic record
Abstract
This study examines the colonization of barley straw (BS) and corn stover (CS) by rumen bacteria and how this is impacted by ammonia fiber expansion (AFEX) pre-treatment. A total of four ruminally cannulated beef heifers were used to investigate in situ microbial colonization in a factorial design with two crop residues, pre-treated with or without AFEX. Crop residues were incubated in the rumen for 0, 2, 4, 8 and 48 h and the colonizing profile was determined using 16 s rRNA gene sequencing. The surface colonizing community clustered based on incubation time and pre-treatment. Fibrobacter, unclassified Bacteroidales, and unclassified Ruminococcaceae were enriched during late stages of colonization. Prevotella and unclassified Lachnospiraceae were enriched in the early stages of colonization. The microbial community colonizing BS-AFEX and CS was less diverse than the community colonizing BS and CS-AFEX. Prevotella, Coprococcus and Clostridium were enriched in both AFEX crop residues, while untreated crop residues were enriched with Methanobrevibacter. Several pathways associated with simple carbohydrate metabolism were enriched in the primary colonizing community of AFEX crop residues. This study suggests that AFEX improves the degradability of crop residues by increasing the accessibility of polysaccharides that can be metabolized by the dominant taxa responsible for primary colonization.
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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 | 0.000 | 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".