A rapid virulence assay for the <scp>D</scp> utch elm disease fungus <i> <scp>O</scp> phiostoma novo‐ulmi </i> by inoculation of apple ( <i> <scp>M</scp> alus </i> × <i> domestica</i> ‘ <scp>G</scp> olden <scp>D</scp> elicious’) fruits
Bibliographic record
Abstract
Large‐scale virulence tests using trees or saplings are expensive, time‐consuming and require a considerable amount of space. The suitability of using ‘ G olden D elicious’ apples as a rapid screen for identifying O phiostoma novo‐ulmi transformants with reduced virulence was thus evaluated. When a collection of O . novo‐ulmi field isolates belonging to subspecies novo‐ulmi or americana was inoculated to apples, members of subsp. novo‐ulmi induced, on average, larger necrotic lesions than subsp. americana isolates. The size of the lesions on apples was not correlated with mycelial growth rate of isolates on nutrient agar. Insertional mutants from O . novo‐ulmi subsp. novo‐ulmi isolate H 327 were inoculated to ‘ G olden D elicious’ apples and U lmus parvifolia × U . americana saplings in parallel experiments. Results clearly indicated that the O . novo‐ulmi transformants included several exhibiting significantly altered levels of virulence. Variability among replicates within a treatment was reduced in apple inoculation data compared to elm sapling data. Overall, the ‘ G olden D elicious’ apple assay was found to be an excellent means for rapidly assessing the virulence level of O . novo‐ulmi isolates.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".