Effect of Amount of DNA on Digital PCR Assessment of Genetically Engineered Canola and Soybean Events
Bibliographic record
Abstract
Abstract Low-level detection and quantification of genetically engineered (GE) traits with polymerase chain reaction (PCR) is challenging. For unapproved GE events, any level of detection is not acceptable in some countries because of zero tolerance. Droplet digital PCR (ddPCR) has been successfully used for absolute quantification of GE events. In this study, reliability of low level quantification of GE events with ddPCR was assessed using a total of 50, 100, 200, 400, and 600 ng DNA spiked at 0.01% and 0.1% concentration levels. Genetically engineered canola (GT73 and MON88302 events) and soybean (A2704-12 and DP305423 events) events were used for the study. For samples spiked at 0.1% level, reliable quantification was achieved for the four GE events using 50 or 100 ng DNA. Few target droplets were generated for 0.01% spiked GE samples using 50 and 100 ng DNA. Increasing the amount of DNA for ddPCR generated more number of target droplets. For GE canola events, the use of 400 and 600 ng DNA for ddPCR resulted in saturation. The use of multiple wells of 200 ng DNA (instead of 400 and 600 ng per well) helped to overcome the saturation problem. Overall, the use of high amount of DNA for ddPCR was helpful for the detection and quantification of 0.01% GE samples.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".