Evaluation of Air Sampling and Detection Methods to Quantify Airborne Ascospores of <i>Sclerotinia sclerotiorum</i>
Bibliographic record
Abstract
Detection and quantification of airborne ascospores as a component of the Sclerotinia rot of carrot (SRC) forecast model is currently accomplished using the blue plate test (BPT), which uses Sclerotinia semiselective medium (SSM). A quantitative polymerase chain reaction (qPCR) assay was developed to reduce the time to specifically quantify ascospores of Sclerotinia sclerotiorum from air samples collected using a Burkard Multi-Vial Cyclone Sampler. The qPCR assay was highly sensitive and detected DNA from 0.5 to 5 × 104 ascospores within a linear range (R2 = 0.99). The qPCR assay was used to quantify ascospores of S. sclerotiorum in air samples collected over three growing seasons. Initial SRC disease was observed 8 and 34 days following detection of 9.5 and 2 ascospores m–3 of air, respectively. Results from air samples collected using an Andersen N6 Sampler and the qPCR assay were compared with the BPT. Ascospore counts from a Burkard Sampler coupled with the qPCR assay and the BPT followed similar trends. In general, fewer ascospores were detected and bioaerosol sampling efficiency was low using an Anderson Sampler. Three days were required to confirm the number of ascospores using SSM in the BPT and with an Andersen Sampler, whereas results from a Burkard Sampler coupled with the qPCR assay can provide results within 5 h of air sampling. The choice of method will depend on the available resources.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".