Evaluation of polyclonal-antibody-based immunoassays for detection of <i>Sclerotinia sclerotiorum</i> on canola petals, and prediction of stem rot
Bibliographic record
Abstract
The potential for using polyclonal-antibody-based immunoassays for detection of Sclerotinia sclerotiorum on canola petals as part of a disease prediction model was investigated. A commercial ELISA kit designed for Sclerotinia homoeocarpa was evaluated for specificity to S. sclerotiorum in comparison to other Sclerotinia spp., and known phyllosphere fungi. This polyclonal-antibody-based kit cross-reacted with antigens from other Sclerotinia spp., and fungi, and absorbance values obtained from S. sclerotiorum-infested canola petals were poorly correlated with percentages of infested petals as determined by plating on semi-selective medium, except when petals were incubated at high humidity for 24 h at 20 degrees C-22 degrees C prior to ELISA evaluation. Additional polyclonal antibodies were prepared from mycelial and semi-purified cell wall antigens, and these antibodies were more specific to S. sclerotiorum than the ELISA kit. However, absorbance values obtained from S. sclerotiorum-infested canola petals were poorly correlated with percentages of infested petals as determined by plating on semi-selective medium. The results are discussed in relation to the use of polyclonal-antibody-based immunoassays for the prediction of epidemics or crop risk from sclerotinia stem rot of canola.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".