A comparison of sequence-based polymorphism and haplotype content in transcribed and anonymous regions of the barley genome
Bibliographic record
Abstract
Direct estimates of sequence diversity provides an abundant source of DNA polymorphisms based on single nucleotide polymorphisms (SNPs). The frequency and distribution of nucleotide diversity within 23 genes associated with grain germination in barley were determined in a sample of accessions representing European cultivars, landraces, and wild barley accessions from throughout the fertile crescent. The overall nucleotide diversity ranged from 0.0021 to 0.0189 with a single nucleotide change being detected every 78 bp and insertion-deletion events being observed every 680 bp. Within the cultivated (H. vulgare) genepool, a small number of haplotypes were detected, the total number of haplotypes observed in H. spontaneum was almost double that detected in H. vulgare (46 and 26, respectively). Distinct haplotypes were observed in the H. spontaneum and landrace genepools, which are highly divergent from H. vulgare. A comparison of SNP-based haplotype data with EST-derived SSRs and genomic SSRs revealed a similar trend of decreasing variability in the cultivated genepool. However, the number of unique alleles identified in the cultivated sample was much greater with genomic SSRs (18%) compared with only 2.1% for SNPs and 3.8% for EST-derived SSRs. The potential utility of SNPs and EST-derived SSRs for association mapping in barley is discussed.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".