Development of a polymerase chain reaction assay for detection of three canola transgenes
Bibliographic record
Abstract
Abstract Many countries are developing or implementing regulatory requirements to monitor for the presence of genetically modified (GM) materials in seeds, grain, and derived food products using DNA and protein‐based methods. There is no published report on the detection of different GM transgenes in canola, and this study is aimed at developing qualitative PCR methods for the three major GM transgenes commercially available in canola. Primer sequences were generated from Gen‐Bank and previously published information to develop a polymerase chain reaction (PCR) detection method for Roundup Ready (glyphosate tolerance, GT73 event), Liberty Link (glufosinate ammonium tolerance, HCN92 event), and BX (Bromoxynil tolerance, OXY235 event) canola varieties. On using PCR, two primer pairs for each of the GT73 and HCN92 and one primer pair for OXY235 amplified specific amplicons for the three GM transgenes. All three GM transgenes were detected simultaneously by multiplexing the five primer pairs in a single PCR reaction. Multiplexing of the five primer pairs for DNA prepared at 1% (one GM seed in 99 non‐GM seeds) and 0.5% (one GM seed in 199 non‐GM seeds) levels generated the expected DNA fragments for GT73, HCN92, and OXY235. This information will lay the groundwork for the development of a quantitative PCR assay for canola transgenic events.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".