A high‐throughput turbidimetric method for quantitative preparation of <i>Plasmodiophora brassicae</i> inoculum for bioassays
Bibliographic record
Abstract
Abstract Clubroot caused by Plasmodiophora brassicae is one of the major diseases on cruciferous crops. This disease has been a problem all over the world and led to serious economic losses in cruciferous crop production. The improvement of clubroot management is dependent on the effectiveness of bioassays on pathogenicity or plant resistance. P. brassicae resting spore inoculum prepared from clubroot tissues was widely used in bioassays. Traditionally, resting spore concentration was measured by visual counting with a haemocytometer; however, this method is time‐consuming, labour intensive and with poor repeatability due to the tiny size of resting spores. In this study, we established a turbidimetric method to measure P. brassicae resting spore concentration of the inoculum. A regression curve was generated by plotting optical density of gradiently diluted resting spore suspension against the corresponding resting spore concentrations, which resulted in a logarithmic regression equation. This method was validated and proved to be robust and effective in determining resting spore concentration of different samples, and was further confirmed by bioassay of infection and clubroot development. In conclusion, we established a standard protocol to prepare P. brassicae inoculum and provided an effective method for resting spore concentration determination. The results of this study can be widely used in research activities on clubroot sustainable management when an effective assessment of P. brassicae biomass is needed.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".