MétaCan
Menu
Back to cohort
Record W2035293279 · doi:10.1021/es8001162

Application of Solid-Phase Microextraction for In Vivo Laboratory and Field Sampling of Pharmaceuticals in Fish

2008· article· en· W2035293279 on OpenAlexafffund
Simon Ningsun Zhou, Ken D. Oakes, Mark R. Servos, Janusz Pawliszyn

Bibliographic record

VenueEnvironmental Science & Technology · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicPharmaceutical and Antibiotic Environmental Impacts
Canadian institutionsUniversity of Waterloo
FundersCanadian Water Network
KeywordsSolid-phase microextractionChromatographyIn vivoChemistryEnvironmental scienceEnvironmental chemistryGas chromatography–mass spectrometryMass spectrometryBiotechnologyBiology

Abstract

fetched live from OpenAlex

Previous field studies utilizing solid-phase microextraction (SPME) predominantly focused on volatile and semivolatile compounds in air or water. Earlier in vivo sampling studies utilizing SPME were limited to the liquid matrix (blood). The present study has expanded the SPME technique to semisolid tissues under laboratory and field conditions through the investigation of both theoretical and applied experimental approaches. Pre-equilibrium extraction and desorption were performed in vivo in two separate animals. Excellent linearity was found between the amounts extracted by SPME from the muscle of living fish and the waterborne concentrations of pharmaceuticals. A simple SPME method is also described to simultaneously determine free and total analyte concentrations in living tissue. The utility of in vivo SPME sampling was evaluated in wild fish collected from a number of different river locations under varying degrees of influence from municipal wastewater effluents. Diphenhydramine and diltiazem were detected in the muscle of fish downstream of a local wastewater treatment plant. Based on this study, SPME demonstrated several important advantages such as simplicity, sensitivity, and robustness under laboratory and in vivo field sampling conditions.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.105
Threshold uncertainty score0.812

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
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 head0.354
Teacher spread0.329 · 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

Citations133
Published2008
Admission routes2
Has abstractyes

Explore more

Same venueEnvironmental Science & TechnologySame topicPharmaceutical and Antibiotic Environmental ImpactsFrench-language works237,207