Biodegradation of genetically modified seeds and plant tissues during composting
Bibliographic record
Abstract
BACKGROUND: The increasing global market of genetically modified (GM) crops amplifies the potential for unintentional contamination of food and feed with GM plants. Methods proposed for disposal of crop residues should be assessed to prevent unintended distribution of GM materials. Composting of organic material is inexpensive and location-independent. The objective of this study was to determine the effectiveness of composting for disposal of GM plants in terms of reducing seed viability and promoting the degradation of endogenous as well as transgenic DNA. RESULTS: Duplicate samples of corn kernels, alfalfa leaves, and GM canola seeds, meal and pellets were sealed in porous nylon bags and implanted in duplicate 85,000 kg (initial weight) feedlot manure compost piles. Samples were collected at intervals over 230 days of composing. Canola seeds and corn kernels were not viable after 14 days of composting with temperatures in the piles exceeding 50 degrees C. In all samples, PCR analyses revealed that plant endogenous and transgenic fragments were substantially degraded after 230 days of composting. Southern blotting of genomic DNA isolated from canola seeds identified differences in the persistence of endogenous, transgenic, and bacterial DNA. CONCLUSION: Composting GM and non-GM plant materials with manure rendered seeds non-viable, and resulted in substantial, although not complete, degradation of endogenous and transgenic plant DNA. This study demonstrates that composting could be effective for disposing of GM crops in the event of their inadvertent entry into the food or feed chain.
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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 | 0.000 |
| 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) | 0.001 | 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".