Detecting and Quantifying the Adventitious Presence of Transgenic Seeds in Safflower, Carthamus tinctorius L.
Bibliographic record
Abstract
Safflower ( Carthamus tinctorius L.) is currently being developed as a platform for the production of novel proteins. Methods for detecting and quantifying transgenic safflower are needed to ensure seed quality and to monitor for its adventitious presence. We developed and compared three methods of assaying for transgenic safflower presence in conventional seedlots: field bioassays, enzyme-linked immunosorbent assays (ELISA), and quantitative polymerase chain reaction (Q-PCR). Limits for reliable quantification for both ELISA and Q-PCR are approximately 0.1%, although levels at least as low as 0.02% can be detected by Q-PCR. Levels of quantification for the field bioassay are limited only by space and resources available. Multiple sampling methods to detect and quantify transgenic safflower presence at levels lower than 0.1% were used on field collected samples from a pollen outcrossing experiment to quantify the adventitious presence of transgenic safflower. Taking into account the potential utility and relative advantages or disadvantages of each detection method, it is recommended that the initial testing for the adventitious presence of transgenic seed be carried out using an antibody-based test if available and that Q-PCR-based assays to quantify transgenic proportion be used when it is necessary to identify specific transgenic constructs or if antibody-based assays are not readily available.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".