Identification of New Sources of Resistance to Tan Spot, Stagonospora Nodorum Blotch, and Septoria Tritici Blotch of Wheat
Bibliographic record
Abstract
Leaf spot of wheat (Triticum aestivum L.) in North America consists of a group of diseases involving tan spot [Pyrenophora tritici‐repentis (Died.) Drechs.], Stagonospora nodorum blotch [Phaeosphaeria nodorum (E.Müller) Hedjarroude], and Septoria tritici blotch [Mycosphaerella graminicola (Fückl) J. Schröt. in Cohn]. A complex of these diseases occurs in nature hence managing leaf spots is difficult. Use of resistant cultivars is the most effective and economical means of controlling leaf spot; however, none of the widely grown wheat cultivars in North America show high levels of resistance to these diseases. Hence, this study aimed to identify new sources of resistance to leaf spotting diseases. To achieve this objective, 975 accessions of wheat and its relatives were evaluated for P. tritici‐repentis, race 1, resistance under controlled environments. Of these 975 accessions, 40 selected accessions were further screened against six virulent races (1, 2, 3, 5, 10, and 11) of P.tritici‐repentis and to foliar pathogens P. nodorum and M. graminicola. New sources of resistance effective against the three leaf spotting disease were identified in accessions of T.monococcum L., T. turgidum L., T. dicoccum Schrank ex Schübler, T. dicoccoides (Körn. ex Asch and Graebner) Schweinf., T. timopheevii (Zhuk.) Zhuk., T. spelta L., and T. aestivum L. including synthetic wheat. Resistance was observed in all three ploidy levels of the wheat genome and presently efforts are being made to transfer the leaf spot resistance into adapted wheat and durum cultivars.
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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 | 0.000 |
| 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".