Development and Comparison of Three Diagnostic Immunoassay Formats for the Detection of Azoxystrobin
Bibliographic record
Abstract
The currently accepted method of detection for azoxystrobin, a strobilurin fungicide, involves a labor-intensive organic solvent extraction and gas chromatography analysis. Three diagnostic assay formats, i.e., enzyme-linked immunosorbent assay (ELISA), fluorescence polarization (FP), and time-resolved fluorescence (TR-FIA), were developed and compared with regard to detection and quantification of azoxystrobin in grape extract and river, lake, and well water samples. These three assay formats require no initial sample extraction and were not affected by any of the environmental matrices tested, and each had a linear working range of 0-400 pg/mL. The polyclonal antibodies used for each of the immunoassays were specific to azoxystrobin; that is, the highest cross-reactivity to other pesticides observed was 5.7%. The limits of detection of the immunoassays were similar at 3 (ELISA), 46 (FP), and 28 (TR-FIA) pg/mL, as were the respective IC50 values of 306, 252, and 244 pg/mL. Each of the three immunoassays developed was less labor-intensive and approximately 100-fold more sensitive than the gas chromatographic method. While the three formats were comparable in terms of performance, the fluorescence polarization assay was the least labor-intensive and required the least time to perform.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".