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Record W2018399740 · doi:10.1021/jf052424o

Development and Comparison of Three Diagnostic Immunoassay Formats for the Detection of Azoxystrobin

2006· article· en· W2018399740 on OpenAlexaff
Gordon Furzer, Linda J. Veldhuis, J. Christopher Hall

Bibliographic record

VenueJournal of Agricultural and Food Chemistry · 2006
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPesticide Residue Analysis and Safety
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsAzoxystrobinImmunoassayChromatographyComputer scienceMedicineChemistryBiologyFungicideBotanyImmunology

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.607
Threshold uncertainty score0.092

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.014
GPT teacher head0.205
Teacher spread0.191 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations33
Published2006
Admission routes1
Has abstractyes

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