“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 (R 2 = 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 cm 3 /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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".