Differential production of sclerotia by isolates of<i>Sclerotinia sclerotiorum</i>from Alaska
Bibliographic record
Abstract
Sclerotinia sclerotiorum was isolated from lettuce (Lactuca sativa) grown in Alaska. Sections of cabbage (Brassica oleracea var. capitata) and lettuce were inoculated with isolates A and B. Sclerotia of S. sclerotiorum were counted and weighed after the host tissue decomposed. Isolate A produced a mean of 42 sclerotia weighing 1.9 g on 100 g of cabbage 'Balbro', which was twice the number of sclerotia and triple the biomass produced by isolate B. On three cultivars of lettuce, isolate A had a greater mass and a similar number of sclerotia compared with isolate B. Carrot (Daucus carota subsp. sativus) and celery (Apium graveolens) were also inoculated to measure production of sclerotia. Differences between isolates of S. sclerotiorum were less pronounced on carrot and celery, but the difference between hosts was significant. The mean mass of sclerotia for isolate A was 73 mg on carrot and 18 mg on celery. Isolates A and B were distinct in the rDNA intergenic spacer sequences. Isolate A had identical sequences to those reported for some isolates from United States and Canada, whereas isolate B matched different isolates from United States, Canada, and New Zealand. Both isolates belong to a relatively recently evolved group exhibiting significant associations between genotype and both geography and host species. In agricultural fields, one infection of S. sclerotiorum on lettuce or cabbage can produce hundreds of new sclerotia that may affect levels of disease in future years. Cultural control can include minimizing the amount of sclerotia on crop residue by disking soon after harvest.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".