Validation of extraction methods for diagnosis of the stem and bulb nematode <i>Ditylenchus dipsaci</i>
Bibliographic record
Abstract
The stem and bulb nematode, Ditylenchus dipsaci (Kühn) Filipjev, is a serious threat to many important crops worldwide, including garlic. For this crop, effective detection methods are essential to discard infected seed cloves and avoid contaminated fields. This study compared the efficacy of different extraction methods for soil and garlic tissues infested with this nematode. The Baermann pan, Baermann funnel and sugar flotation were compared on four types of soil (sand, loam, silty clay and muck soil) previously inoculated with D. dipsaci. There was no significant difference between the Baermann methods which recovered an average of 57.2% of the D. dipsaci from soil. The sugar flotation only captured 20.8% of the D. dipsaci added to the soil. Slight variations were observed between soil types, especially when using the Baermann methods to extract nematodes from silty clay or loam. The two Baermann methods were also compared to a sonication technique for the extraction of D. dipsaci from garlic stems, leaves and bulbs. The Baermann methods showed greater sensitivity at low population density while the sonication allowed the recovery of more D. dipsaci at high density. Overall, this study confirmed the validity of the Baermann pan and funnel methods for the extraction of D. dipsaci from soil and garlic tissues. The sugar flotation and sonication procedures yielded significantly less D. dipsaci or had a poorer sensitivity and were not considered adapted for the diagnosis of this species from soil or garlic tissues.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".