Culturing and Screening the Plant Parasitic Nematode <em>Ditylenchus dipsaci</em>
Bibliographic record
Abstract
Plant-parasitic nematodes (PPNs) destroy over 12% of global food crops every year, which equates to roughly 157 billion dollars (USD) lost annually. With a growing global population and limited arable land, controlling PPN infestation is critical for food production. Compounding the challenge of maximizing crop yields are the mounting restrictions on effective pesticides because of a lack of nematode selectivity. Hence, developing new and safe chemical nematicides is vital to food security. In this protocol, the culture and collection of the PPN species Ditylenchus dipsaci are demonstrated. D. dipsaci is both economically damaging and relatively resistant to most modern nematicides. The current work also explains how to use these nematodes in screens for novel small molecule nematicides and reports on data collection and analysis methodologies. The demonstrated pipeline affords a throughput of thousands of compounds per week and can be easily adapted for use with other PPN species such as Pratylenchus penetrans. The techniques described herein can be used to discover new nematicides, which may, in turn, be further developed into highly selective commercial products that safely combat PPNs to help feed an increasingly hungry world.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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 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".