Development of a new field inoculation technique to assess partial resistance in soybean to <i>Sclerotinia sclerotiorum</i>
Bibliographic record
Abstract
The use of appropriate techniques to assess disease resistance is crucial to understanding the genetic control of host resistance to pathogens to develop resistant plant varieties. The objective of this study was to develop and evaluate a novel field-based inoculation technique for sclerotinia stem rot in soybean [Glycine max (L.) Merr.], caused by Sclerotinia sclerotiorum (Lib.) de Bary. The barley kernel inoculation technique involves stem inoculation using an infected barley kernel inserted into a wound made in the soybean stem. The damage on soybean plants is measured by the length of the lesion on inoculated stem and expressed as inoculation severity index (ISI). Soybean varieties were evaluated for 2 yr at different sites using the barley kernel inoculation technique, and compared with other controlled environment and natural infection-based techniques. The ISIs obtained at Elora, ON, were significantly correlated (r = 0.21, P = 0.021) between the 2 test years (1998 and 1999), and in 1998 with natural infection [as measured by the disease severity index, DSI] at the same location (r = 0.31; P = 0.0005). Using a small set of lines that were specifically selected over several years to respond to DSI, the correlation between the DSI from some locations and years and the ISI in 1999 was negative. The significant positive correlations obtained using soybean lines that were not previously selected for DSI suggest the potential of this technique for pre-screening the breeding material for partial resistance to sclerotinia stem rot prior to confirmation with natural infection levels as measured by DSI. This may be of value to soybean breeders trying to develop soybean lines partially resistant to sclerotinia stem rot because of the difficulty in obtaining consistent and reliable DSI scores across locations and over years. Key words: Sclerotinia stem rot, white mould, inoculation, resistance, soybean
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".