Oligonucleotide Array Approaches for the Detection of Plant Pathogenic Fungi
Bibliographic record
Abstract
Molecular biology techniques are the most powerful approach for the detection of fungal plant pathogens. Polymerase chain reaction (PCR) is one of the most widely used technology for fungal disease identification. However, it is restricted to the detection of one or two pathogens at a time and requires skilled personnel and a laboratory to detect. Over the past few decades, DNA oligonucleotide array techniques such as macroarray and microarray have played a key role in deciphering the underlying mechanism of gene regulation, defense genes, to understand the signal transduction processes, and so on due to their robustness and multiplexing capacity. So far, only a few studies have employed oligonucleotide techniques to screen fungal plant pathogens although their potential for use is immense. In this chapter, an overview of the application of these techniques in plant-fungal phytopathogen diagnosis, methodology, and the prospective of this technology is discussed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.015 | 0.019 |
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".