Evidence map›Paper›PMID 42437349›Full record

ArticleEnvironmental science & technology2026

Knowledge-Based Feature Selection Substantially Enhances Data-Driven Wastewater Treatment Modeling.

Senyuan Gu, Shuting Wang, Ruihong Qiu, Kaili Li, Jaswinder Manjeet Singh, Jue Zhang, Bing-Jie Ni, T David Waite, Liu Ye, Haoran Duan

Abstract read
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In one paragraph

Article in Environmental science & technology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Senyuan GuUNSW Water Research Centre, School of Civil and Environmental Engineering, The University of New South Wales, Sydney, New South Wales 2052, Australia.
Shuting WangSchool of Chemical Engineering, The University of Queensland, Brisbane, Queensland 4072, Australia.
Ruihong QiuSchool of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Queensland 4072, Australia.
Kaili LiSchool of Chemical Engineering, The University of Queensland, Brisbane, Queensland 4072, Australia.ORCID 0000-0002-1676-1475
Jaswinder Manjeet SinghGold Coast City Council, Southport, Queensland 4215, Australia.
Jue ZhangCollege of Geo-informatics, Zhejiang University of Technology, Hangzhou, Zhejiang Province 310000, P. R. China.
Bing-Jie NiUNSW Water Research Centre, School of Civil and Environmental Engineering, The University of New South Wales, Sydney, New South Wales 2052, Australia.
T David WaiteUNSW Water Research Centre, School of Civil and Environmental Engineering, The University of New South Wales, Sydney, New South Wales 2052, Australia.ORCID 0000-0002-5411-3233
Liu YeSchool of Chemical Engineering, The University of Queensland, Brisbane, Queensland 4072, Australia.ORCID 0000-0001-5478-0864
Haoran DuanUNSW Water Research Centre, School of Civil and Environmental Engineering, The University of New South Wales, Sydney, New South Wales 2052, Australia.ORCID 0000-0001-6679-3240

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Data-driven modeling in wastewater treatment is increasingly constrained by the reality of small, high-dimensional data, where the abundant monitoring parameters in small-sized data sets obscure fundamental mechanistic understandings. This study proposes a knowledge-driven feature selection framework that integrates mechanistic insights with statistical correlations to identify the most informative predictive features. Using nitrous oxide (N

Indexed as

WastewaterAlgorithmsLarge Language ModelsNitrous OxideNitrous OxideWastewaterdata drivenexpert knowledgeFeature selectionhigh-dimensionality datamachine learningwastewater modeling

Identifiers

PMID42437349
PMCPMC13394409

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.