A Quantitative Approach to Detect and Overcome PCR Inhibition in Ancient DNA Extracts
Bibliographic record
Abstract
Inhibition is problematic in many applications of PCR, particularly those involving degraded or low amounts of template DNA, when simply diluting the extract is undesirable. Two basic approaches to monitoring inhibition in such samples using real-time or quantitative PCR (qPCR) have been proposed. The first method analyzes the quantification cycle (Cq) deviation of a spiked internal positive control. The second method considers variations in reaction efficiency based on the slopes of individual amplification plots. In combining these methods, we observed increased Cq values together with reduced amplification efficiencies in some samples, as expected; however, deviations from this pattern in other samples support the use of both measurements. Repeat inhibition testing enables optimization of PCR facilitator combinations and sample dilution such that DNA yields and/or quantitative accuracy can be maximized in subsequent PCR runs. Although some trends were apparent within sample types, differences in inhibition levels, optimal reactions conditions, and expected recovery of DNA under these conditions suggest that all samples be routinely tested with this approach.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| 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".