Phenotypic Variability and Genetic Diversity in a Pinus koraiensis Clonal Trial in Northeastern China
Bibliographic record
Abstract
Combining phenotypic and genetic characteristics in a genetic variation study is of paramount importance to effectively orient the selection of producers’ elite trees in a seed orchard. In total, 28 phenotypic characteristics and 16 microsatellite loci were used to analyze the clonal genetic variation, to characterize the genetic diversity, and to refine the genetic classifications of 110 Pinus koraiensis clones grown in the Naozhi orchard in northeastern China. All clones were significantly different in most traits. Most of the phenotypic characteristics showed great genetic variation among clones, while the genotypic differentiation was weak between the selection sites of clones. The SSR markers showed a relatively high level of genetic diversity (Na = 4.67 ± 0.43, Ne = 2.916 ± 0.18, I = 1.15 ± 0.07, Ho = 0.69 ± 0.04, He = 0.62 ± 0.02, and mean polymorphic information content (PIC) of 0.574), with higher heterozygosity as an indication of a lower probability of inbreeding events in the orchard. Despite weak correlation coefficients between dissimilarity matrices (r(A/B), range equal to 0.022, p-value < 0.001), the genetic and phenotypic classifications congruently subdivided all the clones into three major groups. The patterns of phenotypic trait variations and genetic diversity are valuable to effectively select materials in breeding programs of P. koraiensis.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".