Double artificial inoculation of<i>Puccinia triticina</i>for the study of wheat leaf rust resistance
Bibliographic record
Abstract
Artificial inoculation is required for most studies of cereal rust diseases. The techniques used to inoculate Puccinia triticina Eriks., the causal agent of wheat leaf rust, have been successfully employed for decades without many alterations. Field experiments are often exposed to natural as well as artificial infection. Greenhouse experiments are usually limited by space, particularly if inoculations are conducted on adult plants. Multiple genes present in a cultivar can be differentiated by inoculating the cultivar with different races. This is usually done by inoculating one set of plants with one race and a second set with a second race. A technique was developed to inoculate two or more P. triticina races on a single plant, which saved time, growing space and effort. The difference between rust inoculations with a single race and the double-inoculation technique is that the plants are subjected to two cycles of inoculation. Tillers were divided and covered with vinyl cylinders to protect them from the inoculum spray when they were not being inoculated. The double-inoculation technique allowed perfect differentiation between resistant and susceptible rust reactions on a single plant. This technique is a good method to study adult plant resistance.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".