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Record W2035165094 · doi:10.1021/jf072408m

Effects of Postharvest Preparation on Organophosphate Insecticide Residues in Apples

2008· article· en· W2035165094 on OpenAlexaff
Dorothea F.K. Rawn, Sue C. Quade, Wing‐Fung Sun, Mark A. Smith, A. Fouquet, André Bélanger

Bibliographic record

VenueJournal of Agricultural and Food Chemistry · 2008
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPesticide Residue Analysis and Safety
Canadian institutionsHealth Canada
Fundersnot available
KeywordsPostharvestPhosaloneOrchardChemistryHorticultureOrganophosphatePesticideToxicologyAgronomyBiologyChemical control

Abstract

fetched live from OpenAlex

Apples were sampled directly from orchard trees at 96, 45, and 21 days postapplication with one of three organophosphate insecticides (azinphos methyl, phosalone, or phosmet, respectively). Individual apples were prepared for analysis following one of three postharvest preparations: no preparation, rinsed with deionized water for 10-15 s, or rinsed and peeled. Azinphos methyl, phosalone, and phosmet concentrations ranged from below the level of detection to 5.26 ng/g, 94.7 to 5720 ng/g, and 0.011 to 663 ng/g in the apples that received no postharvest preparation, respectively. Although rinsed apples had lower maximum concentrations than observed in apples with no preparation, levels were not significantly lower. Concentrations of all three OP insecticides in apples that were rinsed followed by peeling, however, were much lower (below detection limits to 0.733 ng/g, azinphos methyl; 0.322-219 ng/g, phosalone; and below detection limits to 44.0 ng/g, phosmet) than observed in apples that had been rinsed alone. Rinsing and peeling of apples resulted in a 74.5-97.9% reduction in OP residues, while rinsing alone lowered mean concentrations by 13.5-28.7% relative to apples that received no postharvest preparation.

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 score0.555
Threshold uncertainty score0.148

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 head0.008
GPT teacher head0.191
Teacher spread0.183 · 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

Citations9
Published2008
Admission routes1
Has abstractyes

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