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Record W2007701438 · doi:10.2166/ws.2013.172

Transformation products of pharmaceutically active compounds during drinking water ozonation

2013· article· en· W2007701438 on OpenAlexafffund
Leila Tootchi, Rajesh Seth, Shahram Tabe, Paul Yang

Bibliographic record

VenueWater Science & Technology Water Supply · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicPharmaceutical and Antibiotic Environmental Impacts
Canadian institutionsMinistry of the Environment, Conservation and ParksUniversity of Windsor
FundersPublic Health Agency of CanadaUniversity of Windsor
KeywordsOzoneChemistryHydroxyl radicalDegradation (telecommunications)Environmental chemistryRadicalPhotochemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Ozonation and ozone-based advanced oxidation processes have been shown to be effective in the oxidation of several pharmaceutically active compounds (PhACs) routinely detected in surface waters. Under typical operating conditions of these processes, most of the parent compound oxidized is expected to lead to the formation of transformation products (TPs). For a target ozone exposure, the resulting hydroxyl radical exposure depends on the water matrix or process chosen (e.g. peroxone) which in turn may influence the degradation pathway and the TPs formed. This study was undertaken to examine the expected impact that varying ozone and hydroxyl radical exposures may have on TP formation from the oxidation of PhACs during typical drinking water ozonation. Two representative PhACs were selected for the study. Carbamazepine was chosen to represent PhACs with a fast reaction rate with ozone (kO3 > 104 M−1 s−1) and bezafibrate was chosen to represent PhACs with a slow to moderate reaction rate with ozone (kO3 < 104 M−1 s−1). The results show that under varying ozone and hydroxyl exposure scenarios examined, the major oxidation pathway for the parent compound was dominated by reaction with ozone for carbamazepine while for bezafibrate it varied.

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 categoriesInsufficient payload (model declined to judge)
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 score0.012
Threshold uncertainty score0.999

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 studies0.0000.003
Scholarly communication0.0000.002
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.002

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 head0.012
GPT teacher head0.245
Teacher spread0.233 · 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; both teacher heads agree on what is shown here.

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

Citations14
Published2013
Admission routes2
Has abstractyes

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