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Record W1992787655 · doi:10.4236/ajac.2011.28112

Phenoxyacid Herbicides in Stormwater Retention Ponds: Urban Inputs

2011· article· en· W1992787655 on OpenAlexafffund
Renata Raina, Michele L. Etter, Katherine Buehler, Kevin Starks, Ywomo Yowin

Bibliographic record

VenueAmerican Journal of Analytical Chemistry · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicPesticide and Herbicide Environmental Studies
Canadian institutionsUniversity of Regina
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMecopropSurface runoffStormwaterEnvironmental scienceSurface waterPollutionWater qualityUrban streamEnvironmental chemistryUrban runoffSoil waterWater pollutionHydrology (agriculture)Environmental engineeringPesticideChemistryAgronomyEcologySoil scienceBiology

Abstract

fetched live from OpenAlex

Surface water runoff from urban centers is a major source of environmental pollution which impacts water quality in downstream aquatic habitats. Phenoxyacid herbicides are some of the most widely globally used herbicides in agriculture and urban environments for weed control. Their transformation products which in- clude chlorophenols can be more toxic than the active ingredients. We used LC/MS/MS to analyzed simul- taneously these acid herbicides and their transformation products in stormwater retention ponds taken from an urban environment to examine the occurrence and potential release of these herbicides from urban inputs into downstream waters. 2,4-dichlorophenoxyacetic acid and mecoprop were detected in all samples col- lected from the ponds and at the highest concentrations, while 2-methyl-4-chlorophenoxyacetic acid was de- tected only in spring and summer. Two transformation products, 4-chloro-2-methylphenol and 2,4-di- chlorophenol were detected in samples primarily at inlet locations on the ponds indicating that degradation had occurred in surface soils prior to surface water runoff.

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 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 score0.033
Threshold uncertainty score0.994

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.000

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 head0.016
GPT teacher head0.214
Teacher spread0.198 · 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

Citations11
Published2011
Admission routes2
Has abstractyes

Explore more

Same venueAmerican Journal of Analytical ChemistrySame topicPesticide and Herbicide Environmental StudiesFrench-language works237,207