Evidence map›Paper›PMID 42315862›Full record

ArticleScientific reports2026

CrayStack: a simplified crayfish optimization driven stacking ensemble for prediction of machining quality characteristics under data scarcity.

Ithipri Emonena, Xu Yong, Festus I Ashiedu, Ikuobase Emovon, Olusegun D Samuel, Manjunath Patel G C, Christopher C Enweremadu, Choon Kit Chan

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 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

8 authors.

Ithipri EmonenaDepartment of Mechanical Engineering, Federal University of Petroleum Resources, P.M.B, Effurun, 1221, Delta State, Nigeria.
Xu YongSchool of Artificial Intelligence and Smart Manufacturing, Hechi University, Yizhou, China. xuyong@hcnu.edu.cn.
Festus I AshieduDepartment of Mechanical Engineering, Federal University of Petroleum Resources, P.M.B, Effurun, 1221, Delta State, Nigeria.
Ikuobase EmovonDepartment of Mechanical Engineering, Federal University of Petroleum Resources, P.M.B, Effurun, 1221, Delta State, Nigeria.
Olusegun D SamuelDepartment of Mechanical Engineering, Federal University of Petroleum Resources, P.M.B, Effurun, 1221, Delta State, Nigeria. samuel.david@fupre.edu.ng.
Manjunath Patel G CDepartment of Mechanical Engineering, Sahyadri College of Engineering & Management, Mangaluru, Visvesvaraya Technological University, Belagavi, India. manju09mpm05@gmail.com.
Christopher C EnweremaduDepartment of Mechanical, Bioresources and Biomedical Engineering, University of South Africa, Science Campus, Johannesburg, 1710, FL, South Africa.
Choon Kit ChanMechanical Engineering Department, INTI International University, Nilai, 71800, Negeri Sembilan, Malaysia.

Funding

Guangxi Science and Technology Program No. AA24010001Hechi University No.2024KY0624
6 · The paper itself

Abstract

Intelligent manufacturing demands accurate prediction of machining quality characteristics with conflicting behaviour, which is challenging with limited experimental data. Taguchi L

Indexed as

Crayfish Optimization AlgorithmEnsemble learningMachining process modelingProcess InnovationStacking

Identifiers

PMID42315862
PMCPMC13550608

What OpenQuestion holds

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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.