A reproducible assay for measuring partial resistance to <i>Sclerotinia sclerotiorum</i> in soybean
Bibliographic record
Abstract
Bastien, M., Huynh, T. T., Giroux, G., Iquira, E., Rioux, S. and Belzile, F. 2012. A reproducible assay for measuring partial resistance to Sclerotinia sclerotiorum in soybean. Can. J. Plant Sci. 92: 279–288. In eastern Canada, Sclerotinia stem rot in soybean is an important disease, and resistance is systematically assessed in cultivar performance trials. The reference method used in these trials closely mimics the natural infection process, but is very demanding, and its success is highly subject to environmental conditions. Here we describe a simple, quick and reproducible inoculation method to measure resistance to pathogen progression on the main stem. Importantly, this method is the first to reproducibly identify quantitative trait loci (QTLs) conferring partial resistance to Sclerotinia stem rot in soybean. In this method, a cotton pad saturated with a mycelial suspension is applied to a floral bud and resistance is estimated by measuring lesion length on the main stem. The method was found to discriminate clearly between known resistant and susceptible checks in both the field and greenhouse. Clear discrimination between 26 recombinant inbred lines (RILs) contrasted for QTLs controlling resistance to Sclerotinia stem rot was also achieved in four independent trials, and lesion length was significantly correlated among all trials. When tested on 38, 42 and 40 lines in registration trials, the results of this method were significantly correlated with those of the reference method in 2 of 3 yr.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".