Infection Efficiency of Four Phytophthora infestans Clonal Lineages and DNA-Based Quantification of Sporangia
Bibliographic record
Abstract
The presence and abundance of pathogen inoculum is with host resistance and environmental conditions a key factor in epidemic development. Therefore, several spore-sampling devices have been proposed to monitor pathogen inoculum above fields. However, to make spore sampling more reliable as a management tool and to facilitate its adoption, information on infection efficiency and molecular tools for estimating airborne sporangia concentration are needed. Experiments were thus undertaken in a growth chamber to study the infection efficiency of four clonal lineages of P. infestans (US-8, US-11, US-23, and US-24) by measuring the airborne sporangia concentration and resulting disease intensity. The relationship between the airborne sporangia concentration and the number of lesions per leaf was exponential. For the same concentration, the sporangia of US-23 caused significantly more lesions than the sporangia of the other clonal lineages did. Under optimal conditions, an airborne sporangia concentration of 10 sporangia m-3 for US-23 was sufficient to cause one lesion per leaf, whereas for the other clonal lineages, it took 15 to 25 sporangia m-3 to reach the same disease intensity. However, in terms of diseased leaf area, there was no difference between clonal lineages US-8, US-23 and US-24. Also, a sensitive quantitative real-time polymerase chain reaction (qPCR) tool was developed to quantify P. infestans airborne sporangia with detection sensitivity of one sporangium. The specificity of the qPCR assay was rigorously tested for airborne inoculum and was either similar to, or an improvement on, other published PCR assays. This assay allows rapid and reliable detection and quantification of P. infestans airborne sporangia and thereby, facilitates the implementation of spores-sampling network.
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 | 0.000 | 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) | 0.001 | 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".