<i>In situ</i>identification and quantification of starch-hydrolyzing bacteria attached to barley and corn grain in the rumen of cows fed barley-based diets
Bibliographic record
Abstract
Cereal grains rich in starch are widely used to meet the energy demands of high-producing beef and dairy cattle. Bacteria are important players in starch digestion in the rumen, and thus play an important role in the hydrolysis and fermentation of cereal grains. However, our understanding of the composition of the rumen starch-hydrolyzing bacteria (SHB) is limited. In this study, BODIPY FL DQ starch staining combined with fluorescence in situ hybridization (FISH) and quantitative FISH were applied to label, identify and quantify SHB possessing active cell-surface-associated (CSA) α-amylase activity in the rumen of heifers fed barley-based diets. When individual cells of SHB with active CSA α-amylase activity were enumerated, they constituted 19-23% of the total bacterial cells attached to particles of four different cultivars of barley grain and corn. Quantitative FISH revealed that up to 70-80% of these SHB were members of Ruminococcaceae in the phylum Firmicutes but were not Streptococcus bovis, Ruminobacter amylophilus, Succinomonas amylolytica, Bifidobacterium spp. or Butyrivibrio fibrisolvens, all of whose amylolytic activities have been demonstrated previously in vitro. The proportion of barley grain in the diet had a large impact on the percentage abundance of total SHB and Ruminococcaceae SHB in these animals.
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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".