Clonal Identification Based on Quantitative, Codominant, and Dominant Marker Data: A Comparative Analysis of Selected Willow (<i>Salix</i>L.) Clones
Bibliographic record
Abstract
Clonal identification in forestry may employ different means, each with unique advantages. A comparative evaluation of different approaches is reported. Nine quantitative leaf morphometric parameters, 15 variable codominant (isoenzyme) and 15 variable dominant (RAPD) loci, were used. All clones presented unique multilocus isoenzyme genotypes and 86% presented unique multilocus RAPD genotypes. Quantitative, isoenzyme and molecular data were subjected to principal component analysis, the latter two data sets after vector transformation. Most of the variability (quantitative 99%, isoenzyme 72.5%, RAPD 89%) was accounted for in the first three axes. This study has shown: (1) individual quantitative parameters were inefficient for clonal identification, (2) multilocus clonal identification was successful, (3) dominant markers were more polymorphic than codominant ones: 1.5 variable loci per enzyme system, 7.5 variable RAPD loci per primer, (4) 15 codominant marker loci could identify about 2.8 times more individuals than 15 dominant ones, but this advantage is surpassed when 42 dominant loci are employed, (5) multivariate analysis of morphological, codominant and dominant genetic data could not discriminate at the clonal level. It was concluded that due to their higher number of loci available dominant markers perform better than codominant ones, despite the higher informativeness of the latter.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 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".