Rapid ID Technology (RIDT) in Plants: High-Speed DNA Fingerprinting in Grain Seeds for the Identification, Segregation, Purity, and Traceability of Varieties Using Labautomation Robotics
Bibliographic record
Abstract
A rapid and inexpensive DNA diagnostic platform for fingerprinting Cdn. registered, wheat varieties has been developed. Two current, real-time applications being used in Canada include the determination of purity (% contamination) of grain shipments in rail cars and the monitoring of field plots that represent a midge varietal blend. The quantification of a sample is accomplished by fingerprinting single seeds and a sample having a mixture of varieties can be assayed. The Rapid ID Technology (RIDT) platform enables high-speed, high-throughput, robotic labautomation and low-cost single nucleotide polymorphism (SNP)-DNA fingerprinting in wheat. The inexpensive seed DNA extraction method, rapid PCR amplification, and miniaturization of the Invader assay for SNP scoring are all paramount for fingerprinting millions of single seeds per year in numerous rail cars/field plots at a low cost. The cost factor is the dominant concern in any fingerprinting program and the total cost for consumables for a single marker tested is estimated at less than $0.60 per seed. The RIDT platform can also segregate closely related cultivars, an important characteristic since many varieties have a common genetic background. The results “from seed to fingerprint data” at a central lab can then be transferred electronically to any location using a laboratory information management system.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.009 |
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".