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Record W4223613512 · doi:10.1038/s41545-022-00154-5

Recent developments in hazardous pollutants removal from wastewater and water reuse within a circular economy

2022· article· en· W4223613512 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenpj Clean Water · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicAdvanced oxidation water treatment
Canadian institutionsConcordia University
FundersCenter for Membranes and Advanced Water Technology, Khalifa UniversityKhalifa University of Science, Technology and Research
KeywordsHazardous wasteWastewaterPollutantReuseWaste managementEnvironmental scienceWastewater reuseIndustrial wastewater treatmentHazardous air pollutantsSewage treatmentEnvironmental engineeringEngineeringChemistry

Abstract

fetched live from OpenAlex

Abstract Recent advances in wastewater treatment processes have resulted in high removal efficiencies for various hazardous pollutants. Nevertheless, some technologies are more suitable for targeting specific contaminants than others. We comprehensively reviewed the recent advances in removing hazardous pollutants from industrial wastewater through membrane technologies, adsorption, Fenton-based processes, advanced oxidation processes (AOP), and hybrid systems such as electrically-enhanced membrane bioreactors (eMBRs), and integrated eMBR-adsorption system. Each technology’s key features are compared, and recent modifications to the conventional treatment approaches and limitations of advanced treatment systems are highlighted. The removal of emerging contaminants such as pharmaceuticals from wastewater is also discussed.

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.

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 score0.285
Threshold uncertainty score1.000

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 science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0080.001

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 head0.204
Teacher spread0.193 · 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