Response of wheat genotypes against leaf rust (Puccinia triticina) under field conditions
Bibliographic record
Abstract
Leaf rust occurs worldwide wherever wheat is grown. The current research was planned to evaluate the response of 37 wheat (Triticum aestivum) genotypes against leaf rust resistance under field conditions during crop season 2017-18.These genotypes were sown in augmented design with two meter long line sowing method with 30 cm row to row distance at research area of Agricultural Biotechnology Research Institute (ABRI), Ayub Agricultural Research Institute (AARI), Faisalabad, Pakistan. After every five genotypes, a line of rust spreader “Morocco” was sown. Moreover, the trial was also surrounded by planting two rows of spreader to increase the inoculum pressure. Data was recorded on the basis of leaf rust severity scale. Leaf rust severity response was variable among tested genotypes. Out of thirty-seven genotypes, five wheat lines were immune (no disease symptoms) i.e. 17BT007, 17BT013, 16BT008, 16BT010 and 16BT011. Seven lines were found to be resistant with AUDPC vales ranged 1-199. Response of ten lines was moderately resistant while five lines were categorized as moderately susceptible. Ten genotypes exhibited susceptible response against leaf rust with more than 600 AUDPC value. High values above 600 of AUDPC showed greater incidence of leaf rust on wheat plants while lower AUDPC values indicated resistance to leaf rust. Present research provided the resistant wheat lines to the breeders to incorporate them in their breeding program against leaf rust.
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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".