Integrating Large-Scale Data and RNA Technology to Protect Crops from Fungal Pathogens
Bibliographic record
Abstract
With a rapidly growing human population it is expected that plant science researchers and the agricultural community will need to increase food productivity using less arable land. This challenge is complicated by fungal pathogens and diseases, many of which can severely impact crop yield. Current measures to control fungal pathogens are either ineffective or have adverse effects on the agricultural enterprise. Thus, developing new strategies through research innovation to protect plants from pathogenic fungi is necessary to overcome these hurdles. RNA sequencing technologies are increasing our understanding of the underlying genes and gene regulatory networks mediating disease outcomes. The application of invigorating next generation sequencing strategies to study plant-pathogen interactions has and will provide unprecedented insight into the complex patterns of gene activity responsible for crop protection. However, questions remain about how biological processes in both the pathogen and the host are specified in space directly at the site of infection and over the infection period. The integration of cutting edge molecular and computational tools will provide plant scientists with the arsenal required to identify genes and molecules that play a role in plant protection. Large scale RNA sequence data can then be used to protect plants by targeting genes essential for pathogen viability in the production of stably transformed lines expressing RNA interference molecules, or through foliar applications of double stranded RNA.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".