MétaCan
Menu
Back to cohort
Record W2949561256 · doi:10.1139/er-2019-0007

Processes for the removal of triclosan in the environment and engineered systems: a review

2019· review· en· W2949561256 on OpenAlexvenueno aff
Sikandar I. Mulla, Bahareh Asefi, Ram Naresh Bharagava, Ganesh Dattatraya Saratale, Jiangwei Li, Chu-Long Huang, Chang‐Ping Yu

Bibliographic record

VenueEnvironmental Reviews · 2019
Typereview
Languageen
FieldEnvironmental Science
TopicPharmaceutical and Antibiotic Environmental Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsTriclosanBioaccumulationEnvironmental impact of pharmaceuticals and personal care productsAbiotic componentEnvironmental scienceEffluentSewage treatmentEnvironmental chemistrySewageAquatic environmentWastewaterAquatic ecosystemPollutantEcologyBiologyBiochemical engineeringEnvironmental engineeringChemistry

Abstract

fetched live from OpenAlex

Triclosan (TCS) is a synthetic chlorinated aromatic compound and a typical antibacterial agent widely used in a diverse range of personal care products. Generally, after normal use, TCS is flushed into the sewage system through drainage. However, because of its incomplete removal in wastewater treatment plants, the remaining TCS enters the environmental surroundings via treated effluent as well as through sludge disposal. This not only increases TCS concentrations in the environment, but it can also lead to the bioaccumulation of detectable levels of TCS in food webs from aquatic organisms to humans. Experimental evidence has shown the potential negative effects of TCS and its metabolites to a range of marine and terrestrial organisms. This review systematically summarizes the current state of knowledge on occurrence, negative effects, and degradation mechanisms of TCS by abiotic and biotic processes. We finish by discussing research efforts aimed at identifying knowledge gaps between biochemistry and degradation pathways of TCS.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.980
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.103
GPT teacher head0.346
Teacher spread0.242 · 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.

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

Citations24
Published2019
Admission routes1
Has abstractyes

Explore more

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