Species-specific single nucleotide polymorphism markers for detecting hybridization and introgression in poplarThis article is one of a selection of papers published in the Special Issue on Poplar Research in Canada.
Bibliographic record
Abstract
The increasing use of exotic and hybrid poplar species in forestry and the lack of genetic barriers between most poplar species may present a risk to the genetic integrity of native poplar species. To monitor any spontaneous hybridization and (or) introgression from exotics into native species, it is essential to have a system for the quick and reliable identification of species. We developed a set of single nucleotide polymorphism (SNP) markers that allows the distinction between five commercially important species of poplar ( Populus balsamifera L., Populus deltoides Marsh., Populus trichocarpa Toor. ex Gray, Populus nigra L., and Populus maximowiczii Henry) and their hybrids. Six genomic regions spanning 6.1 kb were screened at the DNA sequence level to search for discriminating SNPs among the five species. A total of 245 SNPs and indels were found, 86 of which were species specific. A subset of 12 species-specific SNPs was chosen for use with high-throughput SNPstream technology. In addition, 32 species-specific SNPs and indels were found that can be assayed using restriction enzymes. Application of the developed markers to a set of hybrid clones showed that the markers are not only useful for monitoring introgression but also for the verification of breeding material.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".