“Piler Dirt” Survey for the Sampling and Detection of Potato Cyst Nematodes
Bibliographic record
Abstract
Potato cyst nematodes are a significant threat to potato production worldwide and have important economic impacts due to yield losses but also because of the expenses associated with regulation procedures. In order to reduce the sampling labor, an alternative strategy named the “Piler Dirt” that collects the soil carried with potato tubers during their transfer to storage was proposed. The method showed a better sensitivity than the reference method to detect fields infested with G. rostochiensis. The quantification of the number of cysts per kilogram of soil was proportional between the two methods at low and moderate population densities (R2 = 0.885) but no correlations were found at high density. However, the quantity of soil generated by the method was exceedingly large to be treated by diagnostic labs. It was shown that subsampling six aliquots, each equivalent to 5,000 cm3/ha, from the total quantity of soil generated by the Piler Dirt method, resulted in a probability of 97% to detect infested fields, 95% of the time in our dataset. Overall, Piler Dirt appears as a good compromise to reduce labor time and cost without significantly affecting sensitivity. However, it will be challenging to implement because it needs to be done simultaneously with harvest and will require the participation of farmers during a busy period.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".