Impacts of Molecular Diagnostic Technologies on Plant Disease Management
Bibliographic record
Abstract
Detection and diagnosis of plant viruses has included serological laboratory tests since the 1960s. Relatively little work was done on serological detection of plant pathogenic bacteria and fungi prior to the development of ELISA and monoclonal antibody technologies. Most applications for laboratory-based tests were directed at virus detection with relatively little emphasis on fungal and bacterial pathogens, though there was some good work done with other groups of plant pathogens. With the advent of molecular biology and the ability to compare regions of genomic DNA representing conserved sequences, the development of laboratory tests increased at an amazing rate for all groups of plant pathogens. Comparison of ITS regions of bacteria, fungi, and nematodes has proven useful for taxonomic purposes. Sequencing of conserved genes has been used to develop PCR-based detection with varying levels of specificity for viruses, fungi, and bacteria. Combinations of ELISA and PCR technologies are used to improve sensitivity of detection and to avoid problems with inhibitors or PCR often found in plants. The application of these technologies in plant pathology has greatly improved our ability to detect plant pathogens and is increasing our understanding of, their ecology and epidemiology.
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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 | 0.013 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".