New Grower-Friendly Methods for Plant Pathogen Monitoring
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
Accurate plant disease diagnoses and rapid detection and identification of plant pathogens are of utmost importance for controlling plant diseases and mitigating the economic losses they incur. Technological advances have increasingly simplified the tools available for the identification of pathogens to the extent that, in some cases, this can be done directly by growers and producers themselves. Commercially available immunoprinting kits and lateral flow devices (LFDs) for detection of selected plant pathogens are among the first tools of what can be considered grower-friendly pathogen monitoring methods. Research efforts, spurned on by point-of-care needs in the medical field, are paving the way for the further development of on-the-spot diagnostics and multiplex technologies in plant pathology. Grower-friendly methods need to be practical, robust, readily available, and cost-effective. Such methods are not restricted to on-the-spot testing but extend to laboratory services, which are sometimes more practicable for growers, extension agents, regulators, and other users of diagnostic tests.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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