Molecular methods for detection of plant pathogensWhat is the future?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Monoclonal antibodies, enzyme-linked immunosorbent assay and DNA-based technologies such as the polymerase chain reaction have been the basis for molecular detection in modern plant pathology. Genomics and biosystematics research are generating fast-growing databases that can be used to design molecular assays for simultaneous detection of a large number of pathogens and beneficial organisms. The medical research field is creating novel platforms with unprecedented capabilities for multiplexing, high throughput and portability, which will provide new opportunities for plant pathology. As new molecular testing devices gain wide acceptance in medical diagnostics, tools for routine monitoring of pathogens and beneficial organisms should become more commonly used in plant pathology if we successfully manage to adapt these technologies to a wide range of microorganisms and substrates.Key words: phylogeny, phylogenomics, molecular ecology, molecular taxonomy, microarrays, DNA arrays, biocomplexity, functional genomics, proteomics applications.
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.
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 | 0.001 | 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 it