Race composition of the loose smut (Ustilago tritici) in Western Siberia
Bibliographic record
Abstract
The development of rational genetic protection for new varieties depends on data on the race composition of the wheat smut pathogen caused by Ustilago tritici, as well as on intrapopulation variability of fungus.The virulence of the isolates was estimated using a differential host series of varieties.Isolates of U. tritici were collected on the fields of the Novosibirsk and Omsk regions, as well as the Altai Krai in 2015-2018.Two sets of a differential host series were used for inoculation.The first one was developed in Russia by V. I. Krivchenko (1987) for identification of loose smut.This set consists of 9 wheat varieties: durum wheat (three entries) and bread wheat (six entries).The another set is Canadian and it's used in foreign studies.It was made by J.J. Nielsen, P. Thomas.(1996) for identification of loose smut and consists of 19 wheat varieties, three of them are durum wheat (TD-1, TD-11, TD-19).A total of 15 isolates collected from different varieties of spring bread wheat were assessed for virulence.As an isolate, one smut spike per plant was taken.The isolate was considered as virulent to the differential host line if more than 10 % of the plants were infected.The races were identified by the key proposed by V.I.Krivchenko and J.J. Nielsen and P. Thomas.In the Novosibirsk region, the race 66 identified on the Russian set dominated.In the Canadian set, it is registered as T-8.The same race was noted in the Altai Krai.Also in these regions, the race 23 was identified.On the Canadian set, it is registered as T-18.The race 12 was identified in the Novosibirsk and Omsk regions.In addition, the race 78 was identified in the Novosibirsk region, the race 58 was identified in the Altai Krai, and the race 1 was identified in the Omsk region.All races were specific for T. aestivum varieties and were not able to infect durum wheat varieties.
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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 | 0.001 | 0.001 |
| 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".