Enumeration of Megasphaera elsdenii in rumen contents by real-time Taq nuclease assay
Bibliographic record
Abstract
AIMS: To develop a real-time Taq nuclease assay (TNA) to enable the in vivo enumeration of Megasphaera elsdenii. METHODS AND RESULTS: Megasphaera elsdenii YE34 was phenotypically characteristic of the species and had 16S rDNA sequence similarity of 98% to previously described isolates. Calibration of the number of cells of M. elsdenii against the cycle threshold of fluorescent dye release gave a straight-line relationship with a correlation coefficient approximating unity. The specificity of the assay for M. elsdenii was confirmed by performing it against a panel of 24 heterogeneous, mainly ruminal bacteria. Megasphaera elsdenii was not detected in ruminal contents from a pasture-fed steer but was readily detected 2 and 50 h after the probiotic introduction of the bacterium into the rumen. CONCLUSIONS: Real-time TNA has provided a sensitive and specific means of enumerating the M. elsdenii population in rumen contents. SIGNIFICANCE AND IMPACT OF THE STUDY: Megasphaera elsdenii is an important lactate-degrading ruminal bacterium that has been selected for probiotic use to prevent acidosis and enhance starch utilization in grain-fed cattle. The assay developed in this study provides a tool for determining the ability of probiotically-introduced M. elsdenii to establish useful populations in the rumen.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".