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Record W4248112001 · doi:10.2134/jeq2007.0524

Pesticide Multiresidues in Waters of the Lower Fraser Valley, British Columbia, Canada. Part I. Surface Water

2009· article· en· W4248112001 on OpenAlexaffabout
Million B. Woudneh, Ziqing Ou, Mark Sekela, Taina Tuominen, Melissa Gledhill

Bibliographic record

VenueJournal of Environmental Quality · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicPesticide and Herbicide Environmental Studies
Canadian institutionsEnvironment and Climate Change CanadaAXYS Technologies (Canada)
Fundersnot available
KeywordsPesticideSimazineDiazinonEndosulfanEnvironmental chemistryEnvironmental scienceSurface waterPesticide residueGas chromatographyChemistryHydrology (agriculture)ChromatographyEnvironmental engineeringAgronomyBiologyGeology

Abstract

fetched live from OpenAlex

In the period 2003 to 2005, a study was conducted to determine the occurrence, and spatial and temporal distribution of 78 pesticides in surface waters of the Lower Fraser Valley (LFV) region of British Columbia, Canada. A high resolution gas chromatography/electron impact high resolution mass spectrometry (HRGC/[EI]HRMS) method capable of detecting analytes at the subnanograms per liter level was developed for this study. Samples were collected and analyzed from three reference, five agricultural and two urban sites. Endosulfan sulfate was detected in all samples collected during the study period including the samples from the reference sites. The maximum concentration of a pesticide detected at the reference sites was 0.261 ng L(-1) for beta-endosulfan. Over the study period, the numbers of pesticides detected at the agricultural sites ranged from 22 to 33 of which 20.8 to 40.9% had a 100% detection frequency. At the agricultural sites, the greatest concentration was detected for diazinon (12,500 ng L(-1)), followed by linuron (1050 ng L(-1)) and simazine (896 ng L(-1)). The greatest pesticide concentration observed for the urban sites was 90.4 ng L(-1) for simazine followed by diazinon (5.39 ng L(-1)). With few exceptions, greater concentrations of herbicides were observed for samples collected during spring than for samples collected during fall. Pesticide data presented in this study provide reference levels for future pesticide monitoring programs in the region.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.235
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0050.000

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 head0.010
GPT teacher head0.214
Teacher spread0.204 · 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.

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

Citations47
Published2009
Admission routes2
Has abstractyes

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