A second-generation diagnostic single nucleotide polymorphism (SNP)-based assay, optimized to distinguish among eight poplar (Populus L.) species and their early hybrids
Bibliographic record
Abstract
Rapid identification of Populus L. species and hybrids can be achieved with relatively little effort through the use of primer extension-based single nucleotide polymorphism (SNP) genotyping assays. We present an optimized set of 36 SNP markers from 28 gene regions that diagnose eight poplar species ( Populus angustifolia James, Populus balsamifera L., Populus deltoides Bartram, Populus fremontii Watson, Populus laurifolia Ledeb., Populus maximowiczii Henry, Populus nigra L., and Populus trichocarpa Torr. & Gray). A total of 700 DNA sequences from six Populus species (1–15 individuals per species) were used to construct the array. A set of flanking and probe oligonucleotides was developed and tested. The accuracy of the SNP assay was validated by genotyping 448 putatively “pure” individuals from 14 species of Populus . Overall, the SNP assay had a high success rate (97.6 %) and will prove useful for the identification of all Aigeiros Duby and Tacamahaca Spach. species and their early-generation hybrids within natural populations and breeding programs. Null alleles and intraspecific polymorphisms were detected for a few locus/species combinations in the Aigeiros and Tacamahaca sections. When we attempted to genotype aspens of the section Populus ( Populus alba L., Populus grandidentata Michx., Populus tremula L., and Populus tremuloides Michx.), the success rate of the SNP array decreased by 13 %, demonstrating moderate cross-sectional transferability.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".