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Record W2067931654 · doi:10.1080/03601230600805931

Triazine and Metolachlor Herbicide Residues in Farm Areas of the Lower Fraser Valley, British Columbia, Canada

2006· article· en· W2067931654 on OpenAlexaffabout
M. T. Wan, Jen‐ni Kuo, B. N. Mcpherson, John Pasternak

Bibliographic record

VenueJournal of Environmental Science and Health Part B · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicPesticide and Herbicide Environmental Studies
Canadian institutionsCanadian Sport Centre PacificEnvironment and Climate Change Canada
Fundersnot available
KeywordsTributaryMetolachlorSimazineAtrazineDitchEnvironmental scienceGrowing seasonDrainage basinHydrology (agriculture)Dry seasonSurface runoffAgronomyGeographyPesticideEcologyBiologyGeology

Abstract

fetched live from OpenAlex

Crop soils, ditch sediments and water flowing from several Lower Fraser River (LFR) farm areas of British Columbia, Canada, to salmon tributary streams of that river were sampled in 2004-2005 to quantify for residues of triazine [atrazine, desethylatrazine (a transformation product of atrazine), propazine, and simazine] and metolachlor (a chloroacetamide) herbicides. Average concentrations [microg kg-1 dry weight (d.w.)] of triazine (10,110) and metolachlor (8,910) herbicides detected in crop soils at the start (May 2004, 2005) of the growing season were about 17 and 6 times, respectively, higher than those found for both herbicide groups during (June-Sept, 2004, 2005) the growing season. In contrast, mean concentrations (microg L-1) of triazines (0.092) and metolachlor (0.014) in permanent ditches adjacent to farms were about 7 and 28 times, respectively, lower at the start than during the growing season. Both herbicide groups in ditch sediments were detected only during the growing season at concentrations averaging about 315 microg kg-1 d.w. The risk potential of these herbicides for non-target aquatic organisms inhabiting permanent farm ditches contiguous to tributary streams of the LFR during the growing season is evaluated and discussed.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.243
Threshold uncertainty score0.713

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.225
Teacher spread0.215 · 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

Citations9
Published2006
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Environmental Science and Health Part BSame topicPesticide and Herbicide Environmental StudiesFrench-language works237,207