A Quantitative Approach to Detect and Overcome PCR Inhibition in Ancient DNA Extracts
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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