Automated Method Based in VNTR Analysis for Rickettsiae Genotyping
Bibliographic record
Abstract
A genetic locus named Rc-65, which is 5' adjacent to gene dksA and 3' adjacent to xerC gene, has previously been demonstrated to contain a VNTR with high discriminatory power in several rickettsial strains and thus, potentially useful for genetically similar strains identification. In this work, we present an automated molecular identification method based on capillary electrophoresis separation of VNTRs amplicons. The resulting electropherograms were in agreement with the sequence data obtained in a previous work. The presented genotyping method is fast and suitable for full automation, being a powerful tool for epidemiological surveillance in a large number of samples and enables the detection co-infected samples. The combination of other VNTR loci should improve the discriminatory capacity of this typing system, providing greater resolution and contributing to a more accurate VNTR-based assay. To our knowledge, this is the first automated assay for rickettsial strains identification.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".