Detection of Genetically Modified Canola Using Multiplex PCR Coupled with Oligonucleotide Microarray Hybridization
Bibliographic record
Abstract
A rapid method was developed for concurrent screening of transgenic elements in GM canola. This method utilizes a single multiplex PCR coupled with an oligonucleotide DNA array capable of simultaneously detecting the 12 approved GM canola lines in Canada. The assay includes construct-specific elements for identification of approved lines, common elements (e.g., CaMV 35S promoter, Agrobacterium tumefaciens nos terminator, or nptII gene) for screening of approved or unapproved lines, a canola-specific endogenous gene, and endogenous genes from heterologous crops to serve as additional controls. Oligonucleotide probes were validated individually for functionality and specificity by amplification of specific transgene sequences from appropriate GM canola lines corresponding to each probe sequence, and hybridization of amplicons to the array. Each target sequence hybridized to its corresponding oligonucleotide probe and no significant cross-hybridization was observed. The limit of detection was examined for the GM lines GT73, T45, and MS8/RF3, and was determined to be 0.1%, 0.1%, and 0.5%, respectively, well within the European food and feed labeling threshold level of 0.9% for approved GM product. Practically, the method was demonstrated to be effective for the detection of GM canola in several types of animal feed, as well as in commercial canola meal.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".