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Record W4317425089 · doi:10.3390/w15030398

Advanced Oxidation Processes for Removal of Emerging Contaminants in Water

2023· article· en· W4317425089 on OpenAlexaboutno aff
Huijiao Wang, Yujue Wang, Dionysios D. Dionysiou

Bibliographic record

VenueWater · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicAdvanced oxidation water treatment
Canadian institutionsnot available
Fundersnot available
KeywordsPersulfateSewage treatmentWastewaterAdvanced oxidation processEnvironmental scienceWater treatmentBiochemical engineeringComputer scienceChemistryCatalysisEnvironmental engineeringEngineering

Abstract

fetched live from OpenAlex

This Special Issue includes manuscripts on mechanistic understanding, development, and implementation of advanced oxidation processes (AOPs) for the removal of contaminants of emerging concern in water and wastewater treatment. The main goal was successfully achieved under the joint effort of authors, anonymous reviewers, and editorial managers. Totally, one review and 15 research papers are included in the Special Issue. These are mainly focused on catalyst synthesis, reactor design, treatment performance, kinetic modeling, reaction mechanisms, and by-product formation during electrochemical, photocatalytic, plasma, persulfate, chlorine, ozone-based, and Fenton-related AOPs at different scales. This Special Issue received attention from researchers from different parts of the world such as Argentina, Brazil, Canada, China, Germany, India, Mexico, and the USA. The guest editors are happy to see that all papers presented are innovative and meaningful, and hope that this Special Issue can promote mechanistic understanding and engineering applications of AOPs for the removal of contaminants of emerging concern in water.

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 score0.011
Threshold uncertainty score0.476

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 head0.013
GPT teacher head0.264
Teacher spread0.251 · 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

Citations44
Published2023
Admission routes1
Has abstractyes

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