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Record W2945538560 · doi:10.1139/er-2018-0109

Review of cytostatic wastewater degradation by ozone and advanced oxidation processes: results from Cuban studies

2019· article· en· W2945538560 on OpenAlexvenueno aff
P. García-Lario, O. Ledea Lozano, Eliet Véliz Lorenzo, Mayra Bataller Venta, Yalexmi Ramos Rodríguez, Carlos Castro, C. Gutiérrez Trujillo, Irán Fernández Torres

Bibliographic record

VenueEnvironmental Reviews · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicPharmaceutical and Antibiotic Environmental Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsDispose patternAquatic environmentEnvironmental scienceEnvironmental chemistryBiochemical engineeringHuman healthWastewaterWaste managementEnvironmental engineeringChemistryBiologyEnvironmental healthEngineeringEcologyMedicine

Abstract

fetched live from OpenAlex

Among pharmaceuticals, cytostatics are a category of emerging contaminants that are of particular environmental concern, because of their mutagenic and carcinogenic characteristics. Concern about the presence of these drugs in the environment has increased in the last decades because of their potential harm to aquatic organisms and human health and their long-term cumulative impact on the environment, even at low concentrations in the order of ppb to ppt (μg/L to ng/L). In this paper we provide of an overview of the use of ozone and (or) advanced oxidation processes to eliminate cytostatics from the pharmaceutical wastewaters focusing on studies undertaken in Cuba. The review revealed that these treatments are suitable to degrade several antineoplastic drugs of different chemical structures and to safety dispose of wastewaters to the environment. In addition, we describe the analytical methods used for the determination of the fundamental by-products of applied treatments.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.004
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.029
GPT teacher head0.301
Teacher spread0.273 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations11
Published2019
Admission routes1
Has abstractyes

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Same venueEnvironmental ReviewsSame topicPharmaceutical and Antibiotic Environmental ImpactsFrench-language works237,207