An optimized method for mycelial compatibility testing in Sclerotinia sclerotiorum
Bibliographic record
Abstract
Classification of isolates into mycelial compatibility groups (MCGs) is used routinely in many laboratories as a quick marker for genotyping Sclerotinia sclerotiorum within populations. Scoring each new sample requires optimization of standardized conditions to support adequate growth of all paired isolates. Appropriate conditions for growth are especially important because diverse compatibility reactions are difficult to categorize and score (e.g., in samples from populations with high genetic diversity, such as those that receive immigration from genetically diverse sources or those that deviate from strict clonality). The current standard medium for MCG testing can be inhibitory to isolates from some samples, confounding scoring of compatibility. We identified two foci for optimization: (i) choice of medium, in this experiment, Patterson's medium amended with red food coloring (termed modified Patterson's medium, MPM, the current standard medium) versus potato dextrose agar (PDA) and (ii) amount of McCormick's red food coloring amended to the growth medium. The red food coloring often yields a red reaction line in incompatible interactions; alternative incompatible reactions are a line of thick or thin hyphae. Based on results to date, self-self pairings of S. sclerotiorum are compatible and are a reliable standard for scoring compatible self-nonself mycelial interactions. PDA amended with 75 microl/L of McCormick's red food coloring was identified as optimal for isolates inhibited by MPM from a highly diverse, recombining population sample. This precisely amended PDA was also suitable for isolates from highly clonal populations that were not inhibited by MPM or by higher concentrations of red food coloring. Under the optimized, standardized conditions all paired isolates grew together and produced interactions that could be scored in repeatedly identifiable categories, compatible or incompatible. Workers are advised to optimize conditions before screening a new population sample.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".