Letter Code System of Nomenclature for <i>Puccinia graminis</i> f. sp. <i>avenae</i>
Bibliographic record
Abstract
Current systems that describe the virulence phenotype in Puccinia graminis f. sp. avenae lack a systematic approach for the naming of races or to provide easily made comparisons of virulence among races. A new nomenclature system that simply and systematically characterizes virulence in P. graminis f. sp. avenae is described. The new system has the distinct advantage of providing easily seen relationships among races in contrast to previous nomenclature systems. This allows for easier interpretation of virulence relationships in the oat stem rust population and provides a large amount of virulence information with a minimum of written characters. This system uses single-gene differential lines with the resistance genes Pg1, Pg2, Pg3, Pg4, Pg6, Pg8, Pg9, Pg10, Pg12, Pg13, Pg15, and Pg16, grouped into three subsets of four lines in sequential Pg gene order. By grouping in sequential gene number order, the relationship of the new system to the "standard" system is easily seen. Each race is designated by a three-letter code, based on the seedling reaction (low or high) on 12 differential lines. The letter code nomenclature system is open ended and can be updated easily as new differential genes are identified. This system simply and precisely describes the virulence phenotypes of isolates of P. graminis f. sp. avenae, and allows for easily made comparisons of virulence of isolates collected over time and across geographical locations worldwide.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.032 | 0.034 |
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".