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Record W4404764213 · doi:10.1021/jasms.4c00422

Assessing Antiparasitic Compounds Persistence in Cattle Hair by DART-MS

2024· article· en· W4404764213 on OpenAlexaff
Almir Custodio Batista, Lanaia Ítala Louzeiro Maciel, Yuri Arrates Rocha, Gabriela Guimarães Souza, Boniek G. Vaz, Welber Daniel Zanetti Lopes, Ana Flávia Machado Botelho, Marc Yves Chalom, Andréa Rodrigues Chaves

Bibliographic record

VenueJournal of the American Society for Mass Spectrometry · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPesticide Residue Analysis and Safety
Canadian institutionsIONICS Mass Spectrometry (Canada)
FundersConselho Nacional de Desenvolvimento Científico e TecnológicoFundação de Amparo à Pesquisa do Estado de GoiásCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsChemistryDartDART ion sourcePersistence (discontinuity)AntiparasiticAntiparasitic agentEnvironmental chemistryPharmacologyOrganic chemistry

Abstract

fetched live from OpenAlex

This study introduces an alternative strategy for evaluating antiparasitic persistence compounds in cattle hair by Direct Analysis in Real Time Mass Spectrometry (DART-MS). The developed DART-MS method aimed to determine fenthion, chlorpyrifos, and cypermethrin in cattle hair samples. DART-MS analyses were performed in positive ion mode, and parameters related to the DART source were evaluated. The analytical performance demonstrated the efficiency of the optimized DART-MS method for fenthion, chlorpyrifos, and cypermethrin quantification in the evaluated samples, meeting criteria for precision, accuracy and limits of detection. Overall, the DART-MS method provided a fast and efficient analysis for determination of antiparasitic agents in cattle hair, which contributes to the evaluation of drug administration protocols and dosage intervals, and aids the safety and advancement of the livestock sector.

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)0.001
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 score0.941
Threshold uncertainty score0.214

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
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 head0.024
GPT teacher head0.283
Teacher spread0.259 · 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

Citations5
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueJournal of the American Society for Mass SpectrometrySame topicPesticide Residue Analysis and SafetyFrench-language works237,207