Development and Application of a Multiplex qPCR Method for the Simultaneous Detection and Quantification of <i>Pratylenchus alleni</i> and <i>P. penetrans</i> in Quebec, Canada
Bibliographic record
Abstract
Root lesion nematodes are very common plant-parasitic nematodes that affect a wide range of plants. More than one species can be found simultaneously in a field, and each has a different impact on crop yield. Unfortunately, identifying them using classical morphometric criteria is very difficult and time consuming. The species Pratylenchus alleni was recently observed for the first time in Canada, associated with severe damage in a soybean field in the province of Quebec. The major species, P. penetrans, is also known to be endemic in Quebec but no data exist on its distribution in field crops. This prompted the development of a multiplex quantitative polymerase chain reaction (PCR) assay for the simultaneous detection and quantification of P. alleni and P. penetrans. The method was found to be specific and sensitive, systematically detecting a single larva in a 100-cm3 soil sample with no cross-amplification with other species, even when they outnumbered the target species. An exogenous internal positive control was included in the test to avoid false negatives due to the presence of PCR inhibitors. This assay was used to study the distribution of P. alleni and P. penetrans in 185 soybean fields in the major soybean-producing areas of Quebec during a 3-year survey. Overall, P. penetrans was found in 42% of the fields, P. alleni in 8%, and both species in 4%. The population density of P. alleni in positive fields was still very low, with only a few larvae detected. However, densities of P. penetrans were much higher: the provincial mean was 51.7 nematodes per 100 cm3 of soil (in positive samples), and 8% of the fields (15 of 185) exceeded the theoretical economic threshold. The presence of P. penetrans was also strongly correlated with soil texture, with lighter soil being the most favorable.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".