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Record W1970952440 · doi:10.1021/jf0609431

Quantitative Determination of Pyrethroids, Pyrethrins, and Piperonyl Butoxide in Surface Water by High-Resolution Gas Chromatography/High-Resolution Mass Spectrometry

2006· article· en· W1970952440 on OpenAlexaff
Million B. Woudneh, Daniel R. Oros

Bibliographic record

VenueJournal of Agricultural and Food Chemistry · 2006
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPesticide Residue Analysis and Safety
Canadian institutionsAXYS Technologies (Canada)
Fundersnot available
KeywordsPiperonyl butoxideChromatographyChemistryGas chromatographyDetection limitMass spectrometrySurface waterResolution (logic)Solid phase extractionPesticideEnvironmental chemistryGas chromatography–mass spectrometryExtraction (chemistry)Environmental scienceEnvironmental engineering

Abstract

fetched live from OpenAlex

A new method for determination of pyrethroids, pyrethrins, and piperonyl butoxide (PBO) by high-resolution gas chromatography/high-resolution mass spectrometry (HRGC/HRMS) was developed for surface water samples. The method is based on sampling 100 L of ambient surface water with a solid phase extraction (SPE) technique that uses both wound glass fiber filters for collecting the particulate-associated chemicals and XAD-2 resin for collecting the dissolved chemicals. The method detection limits of the analytes ranged from 0.58 to 8.16 ng/sample, which is equivalent to a detection limit range of 0.0058-0.082 ng/L for a 100 L water sample collected by the SPE technique. The SPE when coupled with HRGC/HRMS was a suitable match for detecting these chemicals at subnanogram per liter ranges that are toxicologically significant to aquatic organisms. To confirm the utility of this method for environmental applications, pyrethroids and PBO were found at subnanogram per liter concentrations in surface water samples collected from five tributaries (primarily urban creeks) of the San Francisco Bay, California.

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 score0.438
Threshold uncertainty score0.274

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 head0.007
GPT teacher head0.194
Teacher spread0.188 · 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

Citations46
Published2006
Admission routes1
Has abstractyes

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