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Record W4205245212 · doi:10.1002/etc.5284

Emerging contaminant exposure to aquatic systems in the Southern African Development Community

2022· article· en· W4205245212 on OpenAlexfundno aff
Kgato P. Selwe, Jessica Thorn, Alizée O. S. Desrousseaux, Caroline E. H. Dessent, J. Brett Sallach

Bibliographic record

VenueEnvironmental Toxicology and Chemistry · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicPharmaceutical and Antibiotic Environmental Impacts
Canadian institutionsnot available
FundersDivision of Mathematical SciencesAfrican Academy of SciencesEconomic and Social Research CouncilDepartment for International DevelopmentInternational Development Research Centre
KeywordsEnvironmental scienceTanzaniaPopulationGeographyEnvironmental protectionEnvironmental planningEnvironmental health

Abstract

fetched live from OpenAlex

The growing production and use of chemicals and the resultant increase in environmental exposure is of particular concern in developing countries where there is rapid industrialization and population growth but limited information on the occurrence of emerging contaminants. Advances in analytical techniques now allow for the monitoring of emerging contaminants at very low concentrations with the potential to cause harmful ecotoxicological effects. Therefore, we provide the first critical assessment of the current state of knowledge about chemical exposure in waters of the Southern African Developmental Community (SADC). We achieved this through a comprehensive literature review and the creation of a database of chemical monitoring data. Of the 59 articles reviewed, most (n = 36; 61.0%) were from South Africa, and the rest were from Botswana (n = 6; 10.2%), Zimbabwe (n = 6; 10.2%), Malawi (n = 3; 5.1%), Mozambique (n = 3; 5.1%), Zambia (n = 2; 3.4%), Angola (n = 1; 1.7%), Madagascar (n = 1; 1.7%), and Tanzania (n = 1; 1.7%). No publications were found from the remaining seven SADC countries. Emerging contaminants have only been studied in South Africa and Botswana. The antiretroviral drug ritonavir (64.52 µg/L) was detected at the highest average concentration, and ibuprofen (17 times) was detected most frequently. Despite being the primary water source in the region, groundwater was understudied (only 13 studies). High emerging contaminant concentrations in surface waters indicate the presence of secondary sources of pollution such as sewage leakage. We identify research gaps and propose actions to assess and reduce chemical pollution to enable the SADC to address the Sustainable Development Goals, particularly Goal 3.9, to reduce the deaths and illnesses from hazardous chemicals and contamination. Environ Toxicol Chem 2022;41:382-395. © 2022 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

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)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.016
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.006
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
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 head0.017
GPT teacher head0.238
Teacher spread0.222 · 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 designObservational
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

Citations46
Published2022
Admission routes1
Has abstractyes

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