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Record W1523585830 · doi:10.7202/705550ar

Participation des radicaux carbonate à l’oxydation de l’atrazine lors de l’ozonation de solutions aqueuses contenant des ions hydrogénocarbonate

2005· article· fr· W1523585830 on OpenAlexaff
P. Niang-Gaye, N. Karpel van Leitner

Bibliographic record

VenueRevue des sciences de l eau · 2005
Typearticle
Languagefr
FieldEnvironmental Science
TopicAdvanced oxidation water treatment
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsChemistryMedicinal chemistryNuclear chemistry

Abstract

fetched live from OpenAlex

L’étude porte sur la détermination de la contribution des espèces O 3 , OH° et CO 3 °- dans la dégradation de l’atrazine lors de l’ozonation de solutions contenant différentes concentrations en ions hydrogénocarbonate et en carbone organique. Le suivi de la concentration en atrazine et en ozone dissous, et les expressions cinétiques ont permis de calculer les concentrations en radicaux hydroxyle et carbonate au cours des réactions. A partir des données expérimentales obtenues sur des eaux pures additionnées de carbone organique et inorganique, les résultats indiquent que l’élimination du micropolluant résulte de l’action de l’ozone (pour une faible part), des radicaux hydroxyle issus de la décomposition de l’ozone, mais aussi pour une part très significative, des radicaux carbonate. La participation des radicaux CO 3 °- diminue lorsque la concentration en carbone organique augmente. Les radicaux carbonate peuvent être responsable de plus de 40 % de la dégradation de l’atrazine lors de l’ozonation en présence de 7 mM d’ions hydrogénocarbonate et 129 µM d’ions glycolate utilisés comme molécule modèle pour l’apport de carbone organique. Les résultats obtenus sur des eaux naturelles confirment les conclusions déduites des expériences sur des eaux de composition connue.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.117
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.006
Scholarly communication0.0000.002
Open science0.0010.000
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 head0.117
GPT teacher head0.343
Teacher spread0.226 · 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 designSimulation or modeling
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

Citations2
Published2005
Admission routes1
Has abstractyes

Explore more

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