A test of the efficacy of whole‐genome amplification on DNA obtained from low‐yield samples
Bibliographic record
Abstract
Abstract Conservation and population genetic studies are sometimes hampered by insufficient quantities of high quality DNA. One potential way to overcome this problem is through the use of whole genome amplification (WGA) kits. We performed rolling circle WGA on DNA obtained from matched hair and tissue samples of North American red squirrels (Tamiasciurus hudsonicus). Following polymerase chain reaction (PCR) at four microsatellite loci, we compared genotyping success for DNA from different source tissues, both pre‐ and post‐WGA. Genotypes obtained with tissue were robust, whether or not DNA had been subjected to WGA. DNA extracted from hair produced results that were largely concordant with matched tissue samples, although amplification success was reduced and some allelic dropout was observed. WGA of hair samples resulted in a low genotyping success rate and an unacceptably high rate of allelic dropout and genotyping error. The problem was not rectified by conducting PCR of WGA hair samples in triplicate. Therefore, we conclude that WGA is only an effective method of enhancing template DNA quantity when the initial sample is from high‐yield material.
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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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".