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.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 0.001 |
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".