16S Ribosomal RNA Sequence—Based Identification of Veterinary Clinical Bacteria
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study evaluated 16S rRNA gene sequence analysis methods as tools for identification of 22 phenotypically difficult to identify veterinary clinical bacterial isolates in a veterinary diagnostic laboratory. The study compared 16S rRNA gene sequencing and conventional phenotypic identification methods. Using 16S rRNA full-gene sequencing, 95% (21/22) of the isolates were identified to the genus level and 86% (19/22) to the species level. The conventional or commercially available manual identification phenotypic characterization methods presumptively identified 91% (20/22) of the isolates to the genus level and 1 isolate to the species level. However, only 55% (12/22) or 4.5% (1/22) of the phenotypic identifications were correct at the genus or species level when they were compared with the 16S rRNA full-gene sequencing. This study also compared 16S rRNA full-gene and partial-gene sequencing. The results demonstrated that the best 16S rRNA gene-sequencing approach is full-gene sequencing because it gives the most precise species identification. Sequencing of the variable regions 1, 2, and 3 of the 16S rRNA gene could be used for tentative identification because the ability of this sequencing to identify bacteria to the genus level is similar to that of the 16S rRNA full-gene sequencing. This method identified only 14% (3/22) isolates differently to the species level compared with the 16S rRNA full gene sequence. Sequencing of the variable regions 7, 8, and 9 is not recommended because it gives more ambiguous identifications. The cost of a 16S RNA full-gene-sequencing analysis was Can 160 dollars and Can 60 dollars for a partial 16S rRNA gene sequence, i.e., sequencing of variable regions 1, 2, and 3 or variable regions 7, 8 and 9.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| 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) | 0.000 | 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 it