Aggressiveness of isolates of five <i>Pythium</i> species on seeds and seedlings of six North American soybean cultivars
Bibliographic record
Abstract
Several species of Pythium cause seed rot and damping-off of soybean in the United States and Canada. The aggressiveness of 14 isolates representing five Pythium species was evaluated on six North American soybean (Glycine max) cultivars based on seed disease in a Petri plate assay and on seedling emergence, plant and root weights, and root rot severity in temperature-controlled greenhouse experiments. In the in vitro assay, the P. aphanidermatum, P. ultimum var. ultimum and P. spinosum isolates caused higher disease severity on germinating seeds than the P. sylvaticum and P. irregulare isolates. In the greenhouse inoculum layer assays, all isolates reduced emergence and tissue weights of at least some cultivars, with significant isolate × cultivar interactions. The aggressiveness of isolates within each species varied significantly based on all or most of the disease parameters measured. At least one isolate from each species reduced emergence by at least 50% on some cultivars, but Pythium ultimum var. ultimum isolate PU 350 caused the most damping-off, stunting and root rot overall. There was a significant negative correlation between root rot severity or seedling emergence (r = −0.93, P < 0.001). ‘Archer’ and ‘Maple Glen’ were more resistant overall than ‘Conrad’, ‘Maple Isle’, ‘Sloan’, ‘Williams’, but some isolates were not very aggressive on any of the cultivars. This study revealed significant variation in the relative ability of the 14 isolates to cause disease and illustrated the importance of identifying aggressive isolates of Pythium species for applications like resistance screening or genetic mapping studies.
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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) | 0.000 | 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 | 0.000 |
| 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".