Efficiency of the different marker systems for estimation of distinctness between sister line wheat cultivars
Bibliographic record
Abstract
A set of 42 winter wheat cultivars developed from nine populations were evaluated for distinctness. Three marker systems: morphological markers, gliadin allele profiles and microsatellites were used to analyze distinctness between sister cultivars. The morphological based distinctness tests for wheat are based on a crop-specific set of characters that comply with UPOV guidelines. The morphological markers were quite informative although they were not capable to estimate the distances/distinctness between two pair of sister cultivars Evropa 90 and Evropa and Novosadska rana 3 and Novosadska rana 2. The gliadins allele profiles were the least efficient to estimate distinctness between sister line cultivars. This system was not capable to distinguish even cultivars developed from different populations. Sister line wheat cultivars were fingerprinted with 19 wheat microsatellites markers. A total of 106 alleles were detected at 19 wheat microsatellite loci, resulting in an average allele number per marker of 5.6. The number of markers was sufficient to distinguish among most sister line cultivars. Only one pair of sister cultivars, Loznicanka and Kosovka, derived from the same cross could not be distinguished. Comparing all marker systems to evaluate distinctness of sister line wheat cultivars the most efficient was microsatellite markers while gliadin allele profiles was the least efficient. Correlations between matrices based on pedigree data and morphological marker, gliadin profiles and microsatellites were significant but not large.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".