Evidence map›Paper›PMID 41114260›Full record

ArticleACS omega2025

Chitosan-Based Flocculant Heavy Metal Removal Prediction Using Machine Learning Models.

Zaher Mundher Yaseen, Ziaul Haq Doost, Rauf Khan, Abdulazeez Abdulraheem, Sajjad Firas Abdulameer, Mayadah W Falah, Aitazaz A Farooque

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in ACS omega, 2025. 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

7 authors.

Zaher Mundher YaseenDepartment of Civil and Environmental Engineering, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia.ORCID https://orcid.org/0000-0003-3647-7137
Ziaul Haq DoostDepartment of Civil and Environmental Engineering, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia.ORCID https://orcid.org/0000-0001-6256-2699
Rauf KhanDepartment of Civil and Environmental Engineering, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia.
Abdulazeez AbdulraheemDepartment of Petroleum Engineering, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia.ORCID https://orcid.org/0000-0002-9994-4436
Sajjad Firas AbdulameerScientific Research Center, Al-Ayen University, Nasiriyah 64001, Thi-Qar, Iraq.
Mayadah W FalahBuilding and Construction Techniques Engineering Department, College of Engineering and Engineering Techniques, Al-Mustaqbal University, Hillah 51001, Babylon, Iraq.
Aitazaz A FarooqueCanadian Centre for Climate Change and Adaptation, University of Prince Edward Island, 550 University Avenue, Charlottetown, PE C1A 4P3, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The expanding impact of heavy metals (HMs) on environmental and public health necessitates the development of advanced predictive models that enhance the precision and efficiency of monitoring and remediation strategies. This study aimed to evaluate newly developed machine learning (ML) models for predicting the removal of HMs such as cadmium (Cd

Identifiers

PMID41114260
PMCPMC12529125

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LicenceCC BY-NC-ND
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Registered trials

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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.