Increasing the Efficiency of Canola and Soybean GMO Detection and Quantification Using Multiplex Droplet Digital PCR
Bibliographic record
Abstract
The number of genetically modified (GM) events for canola, maize, and soybean has been steadily increasing. Real-time PCR is widely used for the detection and quantification of individual GM events. Digital PCR (dPCR) has also been used for absolute quantification of GM events. A duplex dPCR assay consisting of one reference gene and one GM event has been carried out in most cases. The detection of more than one GM event in a single assay will increase the efficiency of dPCR. The feasibility of detection and quantification of two, three, and four GM canola and soybean events at the same time was investigated at 0.1%, 1%, and 5% levels using the QX200 Droplet Digital PCR (ddPCR) system. The reference gene assay was carried out on the same plate but in different wells. For some of the assays, optimization of the probe concentrations and labels was needed for successful ddPCR. Results close to the expected result were achieved for duplex, triplex, and tetraplex ddPCR assays for GM canola events. Similar ddPCR results were also achieved for some GM soybean events with some exceptions. Overall, absolute quantification of up to four GM events at the same time improves the efficiency of GM detection.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.003 | 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".