Optimizing methods to sample and quantify stem and bulb nematode, <i>Ditylenchus dipsaci</i>, in garlic, <i>Allium sativum</i>, field soil
Bibliographic record
Abstract
Stem and bulb nematode (Ditylenchus dipsaci) is a plant parasite that can cause severe damage to garlic crops in Ontario, Canada, and other garlic growing regions. Accurate soil sampling is important to determine the risk of nematode damage before planting garlic in a field. However, it is not clear where the nematode is most concentrated in the soil profile or how the nematode is best extracted. A field survey and laboratory experiments were conducted in the autumn of 2015 and 2018 to determine the distribution of stem and bulb nematode in the soil profile and to determine the most effective extraction method. The soil in 20 garlic fields throughout southern Ontario was sampled and the top 5 cm and the bottom 5–20 cm of soil were collected in a single core and then separated. Nematodes were extracted from all soil samples using both the Baermann pan and sugar centrifugal flotation methods. Significantly more stem and bulb nematodes were extracted from the top 5 cm of soil and using the sugar centrifugal flotation method. An additional extraction efficacy experiment was conducted using a known quantity of stem and bulb nematodes in soil to compare various extraction methods, and the sugar centrifugal flotation method continued to be the more effective method. These results demonstrate that only the top 5 cm of soil should be collected and assessed for populations of stem and bulb nematode in fields intended for garlic production, and the sugar centrifugal flotation method should be the extraction method of choice.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".