Improved template representation in <i>cpn</i> 60 polymerase chain reaction (PCR) product libraries generated from complex templates by application of a specific mixture of PCR primers
Bibliographic record
Abstract
Some classes of high G+C content organisms such as the Actinobacteria, which are known through culture-based studies to be present in large numbers in particular microbial communities, are under-represented or even absent from 16S rRNA or cpn60 polymerase chain reaction (PCR) product libraries derived from these templates. Using reference cpn60 sequence data from organisms with high G+C content genomes, a pair of PCR primers were designed which, when used in combination with the previously developed degenerate, universal cpn60 primers, improve the representation of templates with high G+C content. The primers were validated using a combination of traditional and quantitative real-time PCR on both manufactured template mixtures and biological samples. The development and optimization of this specific primer mixture represents an improvement of established methods and a significant advance in the ability to generate cpn60 PCR product libraries that more closely represent the sequence diversity in complex templates.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".