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Record W4406314591 · doi:10.54254/2755-2721/2025.20020

Report on the Elements and Composition of Emerging Contaminants in Water Bodies of China

2025· article· en· W4406314591 on OpenAlexaff

Bibliographic record

VenueApplied and Computational Engineering · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Quality and Pollution Assessment
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsChinaComposition (language)ContaminationEnvironmental scienceWater contaminationEnvironmental protectionEnvironmental planningBusinessGeographyEcologyBiologyArchaeologyArt

Abstract

fetched live from OpenAlex

In the context of rapid industrialization and urbanization in China over the past few years, the issue of water pollution has emerged as a significant concern. This article provides a comprehensive literature review on the research conducted on water body conditions in China over the past few decades. The review encompasses various studies that have examined the pollution levels of different types of water sources across the country, as well as identified different types of pollutants present in these water bodies. The contamination level is found to be varying over the past 20 to 30 years time, with a continuing increasing focus over the polluting power of neo-pollutants, the PFAS. Continued research efforts are necessary to monitor changes in pollution levels over time accurately. In addition, implementing effective regulations and adopting sustainable practices will be vital steps towards mitigating further degradation of precious water bodies in China.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.083

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.005
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.006
GPT teacher head0.228
Teacher spread0.221 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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