Low level presence of unapproved biotech materials: Current status and capability of DNA-based detection methods
Bibliographic record
Abstract
Demeke, T. and Perry, D. J. 2014. Low level presence of unapproved biotech materials: Current status and capability of DNA-based detection methods. Can. J. Plant Sci. 94: 497–507. In agricultural biotechnology, low level presence (LLP) of recombinant DNA plant material is defined as the unintended presence of trace levels of a specific genetically engineered (GE) or biotechnology-derived material which in most instances has been authorized for use as food or feed in at least one country. Asynchronous authorizations of GE products have prompted testing for the GE content in an assortment of agricultural products for the purpose of facilitating international grain trade. Low level presence of some unauthorized GE materials identified in non-GE grains, oilseeds and food stuffs has negatively impacted grain trade. Other factors contributing to a negative impact on grain trade due to LLP of GE material include zero tolerance policies and slow regulatory approval processes for some countries. This element alone heightens the need for accurate, reliable and cost-effective detection methods. As the number of biotech events increases, the challenge of handling LLP of unapproved GE materials poses an even greater challenge. Polymerase chain reaction (PCR) is widely used for detection and quantification of GE events. Accuracy of PCR-based testing of GE events is affected by variation in sampling, sample preparation and various confounders associated with testing methods. Challenges when using PCR detection and quantification methods for the detection of LLP of GE events are the focus of this review as well as background information and recent examples of occurrence and suggestions to mitigate LLP as it relates to GE materials in grain trade.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.021 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".