Races of <i>Phytophthora sojae</i> and Their Virulences on Soybean Cultivars in Heilongjiang, China
Bibliographic record
Abstract
Phytophthora root and stem rot, caused by Phytophthora sojae, is an economically important disease of soybean (Glycine max) in Heilongjiang Province, China. The objectives of this research were to determine the race profile of P. sojae in Heilongjiang and evaluate soybean cultivars for reactions to the pathogen races. A total of 96 single-zoospore P. sojae isolates were obtained from soil samples collected from 35 soybean fields in 18 counties in Heilongjiang from 2005 to 2007. Eight races of P. sojae, including races 1, 3, 4, 5, 9, 13, 44, and 54, were identified on a set of eight differentials, each containing a single resistance Rps gene, from 80 of the 96 isolates. Races 1 and 3 were predominant races, comprising 58 and 14 isolates, and representing 60 and 7% of the pathogen population, respectively. Races 4, 5, 44, and 54 were identified for the first time in Heilongjiang, and each was represented by two to three isolates only. Sixty-two soybean cultivars commonly grown in Heilongjiang Province were evaluated for their resistance to the eight P. sojae races identified using the hypocotyl inoculation technique. Based on the percentage of plant mortality rated 5 days after inoculation, 44 cultivars were resistant (<30% mortality) to at least one race. These cultivars may be used as sources of resistance in soybean breeding programs.
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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".