Quantitation of Transgenic Plant DNA in Leachate Water: Real-Time Polymerase Chain Reaction Analysis
Bibliographic record
Abstract
Roundup Ready (RR) genetically modified (GM) corn and soybean comprise a large portion of the annual planted acreage of GM crops. Plant growth and subsequent plant decomposition introduce the recombinant DNA (rDNA) into the soil environment, where its fate has not been completely researched. Little is known of the temporal and spatial distribution of plant-derived rDNA in the soil environment and in situ transport of plant DNA by leachate water has not been studied before. The objectives of this study were to determine whether sufficient quantities of plant rDNA were released by roots during growth and early decomposition to be detected in water collected after percolating through a soil profile and to determine the influence of temperature on DNA persistence in the leachate water. Individual plants of RR corn and RR soybean were grown in modified cylinders in a growth room, and the cylinders were flushed with rain water weekly. Immediately after collection, the leachate was subjected to DNA purification followed by rDNA quantification using real-time Polymerase Chain Reaction (PCR) analysis. To test the effects of temperature on plant DNA persistence in leachate water, water samples were spiked with known quantities of RR soybean or RR corn genomic DNA and DNA persistence was examined at 5, 15, and 25 degrees C. Differences in the amounts and temporal distributions of root-derived rDNA were observed between corn and soybean plants. The results suggest that rainfall events may distribute plant DNA throughout the soil and into leachate water. Half-lives of plant DNA in leachate water ranged from 1.2 to 26.7 h, and persistence was greater at colder temperatures (5 and 15 degrees C).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".