Factors Affecting the Presence and Persistence of Plant DNA in the Soil Environment in Corn and Soybean Rotations
Bibliographic record
Abstract
This study investigated factors that influence occurrence and persistence of plant DNA in the soil environment in three crop rotations. In each rotation, soil was sampled in May before planting, in July and August while crops were growing, and in October after harvest. Total DNA was recovered from soil samples taken at two different depths in the soil profile and quantified. Three target plant genes (cornCP4 epsps, corn10-kD Zein, and soybeanCP4 epsps) also were quantified in these DNA extracts using species-specific quantitative real-time PCR assays. In general, total plant DNA content in the soil environment was greatest when the crop was growing in the field and decreased rapidly after harvest. Nevertheless, low levels of target plant DNA were often still detectable the following spring. Age of rotation did not influence target DNA quantities found in the soil environment. Data were collected for a combination of 10 location-years, which allowed for estimation of the variance components for six factors including time of sampling, year, location, crop, sampling depth, and herbicide to total and target DNA content in the soil samples. Mean target recombinant DNA content in soil was influenced most strongly by time of sampling and year (85 and 6%, respectively), whereas total soil DNA content was less dynamic and was most strongly influenced by location and year (49 and 25%, respectively). Over the duration of this study, no accumulation of transgenic plant DNA in the soil environment was observed.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".