Droplet digital PCR for verification of interspersed refuge in midge tolerant wheat varietal blends1
Bibliographic record
Abstract
Abstract: Analysis of bulk ground samples by droplet digital PCR (ddPCR) was investigated as an alternative to individual kernel testing for assessment of interspersed refuge in midge [Sitodiplosis mosellana (Géhin)] tolerant wheat (Triticum aestivum L.) varietal blends. Four genotyping assays were selected such that at least one assay was informative for each of 15 varietal blends registered for production in Canada. The assays were examined in DNA of each of the constituent varieties to assess intravarietal polymorphism and in DNA mixtures simulating each varietal blend. Seed mixtures corresponding to a range of refuge proportions in two different varietal blends were prepared and assessed by ddPCR on two different platforms (RainDrop Digital PCR System, RainDance Technologies, Billerica, MA, and QX200 AutoDG Droplet Digital PCR System, Bio-Rad Laboratories, Hercules, CA). Both systems yielded refuge estimates that were very close to the targeted proportions across the range simulated. Standard deviations among estimates made with the QX200 were, on average, about 60% greater than those among corresponding estimates made with the RainDrop system, but in either case, the coefficients of variation generally remained under 5%. For a ddPCR-based assessment of interspersed refuge in midge tolerant wheat varietal blends, any advantage held by the RainDrop system with respect to precision may be offset by higher throughput achievable with the QX200.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".