Prevalence of inversion positive and inversion negative mating type (<i>MAT</i>) alleles and <i>MAT</i> heterokaryons in <i>Sclerotinia sclerotiorum</i> in the United States
Bibliographic record
Abstract
Sclerotinia sclerotiorum is a yield-limiting pathogen of several economically important crops, and it reproduces sexually by self-fertilization. Based on the presence of an inversion in the mating type locus, S. sclerotiorum can be grouped as inversion negative (Inv– MAT) and inversion positive (Inv+ MAT) isolates. This study was conducted to determine the prevalence of Inv– and Inv+ MAT S. sclerotiorum isolates across the United States. In total, 164 isolates from 16 hosts and 22 states were evaluated, including 87 isolates from North Dakota and northern Minnesota and 47 isolates from soybean. PCR screening was performed separately for Inv– and Inv+ MAT with specific primers. Of the two kinds of MAT homokaryons, Inv– MAT isolates were the most frequent (31.7%) and were identified in 15 states. Another 12.8% of isolates were Inv+ MAT, and were identified in 8 states. The majority (55.5%) of isolates screened were MAT heterokaryons, and these were identified from 18 states and 11 hosts. The implications of MAT heterokaryons for the biology and management of S. sclerotiorum are discussed.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".