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Record W2081762718 · doi:10.1021/jf990618w

Photodegradation of Metolachlor:  Isolation, Identification, and Quantification of Monochloroacetic Acid

2000· article· en· W2081762718 on OpenAlexaff
Ruth I. Wilson, Scott A. Mabury

Bibliographic record

VenueJournal of Agricultural and Food Chemistry · 2000
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Treatment and Disinfection
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMetolachlorChemistryPhotodegradationPhotodissociationAlachlorButachlorSulfuric acidAcetamideChromatographyOrganic chemistryPesticideCatalysisPhotocatalysis

Abstract

fetched live from OpenAlex

The photolysis of metolachlor [2-chloro-N-(2-ethyl-6-methylphenyl)-N-(2-methoxy-1-methylethyl) acetamide] in a sunlight simulator under actinic radiation was investigated. The focus of the study was to determine the extent of monochloroacetic acid (MCA) production. MCA was concentrated and derivatized from photolysate as the n-propyl ester using propanol and sulfuric acid and then identified as the ester using GC/MS and GC/ECD. On the basis of regression analysis, it was shown that the direct photodegradation of approximately 10 microM metolachlor followed pseudo-first-order kinetics with respect to the metolachlor concentration, and the half-life of the herbicide ( approximately 74 h) was independent of the pH of the medium. Photolysis in synthetic field water (SFW) resulted in a significant reduction of photolysis time (t(1/2) approximately 9 h). Direct photolysis experiments indicate a 5.19 +/- 0.81% (n=3) conversion of metolachlor to MCA, while photolysis in synthetic field water and in a Don River water sample resulted in 29.8 +/- 4.6% (n = 3) and 12.6 +/- 4.1% (n = 3) conversion, respectively; MCA was shown to be hydrolytically stable over the time course of the photoreaction. The photodegradation of alachlor, butachlor and a model chloroacetanilide, 2-chloro-N-methylacetanilide, in SFW were also investigated.

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 score0.363
Threshold uncertainty score0.221

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 head0.189
Teacher spread0.182 · 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

Citations53
Published2000
Admission routes1
Has abstractyes

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