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Record W2088496037 · doi:10.1021/jf052413v

Organophosphate Levels in Apple Composites and Individual Apples from a Treated Canadian Orchard

2006· article· en· W2088496037 on OpenAlexaffabout
Dorothea F.K. Rawn, Sue C. Quade, John B. Shields, G. Conca, Wing‐Fung Sun, G. Lacroix, Mark Smith, A. Fouquet, André Bélanger

Bibliographic record

VenueJournal of Agricultural and Food Chemistry · 2006
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPesticide Residue Analysis and Safety
Canadian institutionsHealth Canada
Fundersnot available
KeywordsOrchardOrganophosphateComposite materialHorticultureToxicologyMaterials sciencePesticideBiologyAgronomy

Abstract

fetched live from OpenAlex

Azinphos-methyl, phosalone, and phosmet were applied individually to separate rows of trees within a commercial apple orchard in Quebec, Canada, during the 2003 agricultural season. Apples were collected for residue analysis immediately prior to the harvesting of the remaining apples for market distribution and were prepared for analysis as both individual apples and as composites of eight individuals. Analysis of the three applied compounds, as well as five organophosphate insecticides that were not applied, was performed using gas chromatography-mass spectrometry. Azinphos-methyl, phosalone, and phosmet, which were applied, were detected in all samples analyzed at concentrations ranging from 0.004 ng/g to 2260 ng/g. Methidathion was not observed in any sample. Chlorpyrifos, diazinon, dimethoate, and malathion concentrations ranged from below method detection limits to 0.71 ng/g, and the detection frequency for these compounds ranged from 20% to 100%. Residues measured in this study were all below the Canadian maximum residue limit for apples. Variability factors ranged from 2 to 19 for all compounds observed in this study. Composite samples may not accurately reflect the extremes of exposure from consumption of single servings of apples.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.765
Threshold uncertainty score0.990

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 head0.012
GPT teacher head0.174
Teacher spread0.162 · 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 designObservational
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

Citations29
Published2006
Admission routes2
Has abstractyes

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