Quantitative assessment of phytopathogenic fungi in various substrates using a DNA macroarray
Bibliographic record
Abstract
Detection, identification and quantification of plant pathogens are the cornerstones of preventive plant disease management. To detect multiple pathogens in a single assay, DNA array technology currently is the most suitable technique. However, for sensitive detection, polymerase chain reaction (PCR) amplification before array hybridization is required. To evaluate whether DNA array technology can be used to simultaneously detect and quantify multiple pathogens, a DNA macroarray was designed and optimized for accurate quantification over at least three orders of magnitude of the economically important vascular wilt pathogens Verticillium albo-atrum and Verticillium dahliae. A strong correlation was observed between hybridization signals and pathogen concentrations for standard DNA added to DNA from different origins and for infested samples. While accounting for specific criteria like amount of immobilized detector oligonucleotide and controls for PCR kinetics, accurate quantification of pathogens was achieved in concentration ranges typically encountered in horticultural practice. Subsequently, quantitative assessment of other tomato pathogens (Fusarium oxysporum, Fusarium solani, Pythium ultimum and Rhizoctonia solani) in environmental samples was performed using DNA array technology and correlated to measurements obtained using real-time PCR. As both methods of quantification showed a very high degree of correlation, the reliability and robustness of the DNA array technology is shown.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".