Differentiation of three closely related Japanese oak species and detection of interspecific hybrids using AFLP markers
Bibliographic record
Abstract
Three white-oak species ( Quercus crispula Blume, Quercus dentata Thunb., and Quercus serrata Thunb.) are native, widely distributed, and prominent species in the temperate deciduous forests of Japan. They are closely related to each other and overlapping morphological variation in some traits is observed, although they differ from each other in appearance. To distinguish these species genetically, we carried out clustering analysis based on Bayesian approach by AFLP markers using morphologically typical trees. Although no completely species-specific markers were obtained, these species could be distinguished and their genetic relationships were evaluated based on differences in frequencies of 66 polymorphic markers, including four that were almost completely species-specific. We also attempted to characterize putative interspecific hybrids between Q. crispula and Q. dentata sampled in a mixed stand. Two programs, HINDEX and STRUCTURE, were successfully used to detect several hybrid individuals without any prior information about their morphological traits. However, STRUCTURE and HINDEX gave conflicting indications regarding the admixture levels in some individuals.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 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".