Isolation and linkage analysis of expressed disease-resistance gene analogues of sugar beet (<i>Beta vulgaris</i>L.)
Bibliographic record
Abstract
Sequence conservation among resistance genes (R genes) was exploited to identify 47 R gene analogues (RGAs) from sugar beet (Beta vulgaris L.). Using degenerate primers, 11 RGAs were amplified from genomic DNA and 7 from leaf or beet cDNA. Twenty-nine were selected from an EST sequencing program. Twenty-one RGAs contained structures similar to the nucleotide binding site (NBS)--leucine rich repeat (LRR) domain, a motif commonly found in several R genes. Among the remaining RGAs, 19 revealed similarity to the serine (threonine) protein kinase domain of R genes, 4 showed features related to the LRR region of the rice disease resistance gene Xa21, 1 RGA resembled the sugar beet nematode resistance gene Hs1pro-1, and 2 had homologies to other gene products associated with disease resistance. For 20 EST-derived RGAs, transcript levels were compared in leaf and root tissue revealing organ-specific transcription in 7 cases. Thirty-three RGAs were spread over all nine sugar beet chromosomes, except for a cluster of nine closely linked RGAs on chromosome 7. The analysis of linkage between RGAs and loci for rhizomania and Cercospora resistance identified alleles associated with resistance in both cases.
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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 | 0.000 |
| 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) | 0.001 | 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".