Influence of Amount of Starting Material for DNA Extraction on Detection of Low-Level Presence of Genetically Engineered Traits
Bibliographic record
Abstract
Two laboratories independently examined how the amount of starting material influences DNA extraction efficiency and, ultimately, the detection of low-level presence of genetically engineered (GE) traits in commercialized grains. GE traits from one maize, two canola, and two soybean samples were used as prototypical models in the study design as well as two commonly used DNA extraction methods, a small scale (0.1 and 0.2 g samples) and a large scale (1.0 and 2.0 g samples). The DNA samples were fortified (spiked) at 0.1 and 0.01% (w/w) levels. The amount of DNA recovery varied between the two laboratories, although a sufficient amount of DNA was obtained to perform replicate PCR analysis by both laboratories. Reliable detection of all five events was achieved by both laboratories at 0.1% level using either small-scale or large-scale DNA extractions. Reliable detection of the GE events was achieved at 0.01% level for soybean and canola but not for maize. Variability was observed among the two laboratories in terms of the Ct values generated. There was no difference between small-scale and large-scale DNA extraction methods for qualitative PCR detections of all five GE 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 | 0.008 | 0.015 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".