A second-generation diagnostic single nucleotide polymorphism (SNP)-based assay, optimized to distinguish among eight poplar (Populus L.) species and their early hybrids
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it