Comparison of similarity coefficients used for cluster analysis based on SSR markers in sister line wheat cultivars
Bibliographic record
Abstract
The objective of this study was to compared fourteen different similarity coefficients and their influence in sister line wheat cultivars clustering. Seventeen sister cultivars developed from two crosses were used and fingerprinted with 19 wheat microsatellite markers. Comparisons among the similarity coefficients were made using the Sperman correlation analysis, dendogram evaluation (visual inspection and consensus fork index - CIc), projection efficiency in a two-dimensional space, and groups formed by the Tocher optimization procedure. The Sperman correlation coefficients among the fourteen similarity coefficients were all high showing a strong association between them. The correlation coefficient between Dice and Kulczinski and Ochiai I as well as between Hamann and Simple matching and between Kulczinski and Ochiai I was equal to 1. Although visual estimation of the dendograms shows almost identical clustering structures, CIc indexes indicate that all coefficients are not identical.
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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".