Evidence map›Paper›PMID 42481602›Full record

ArticleScientific reports2026

Machine learning-driven estimation of microplastic percentage yield for rapid and accurate quantification.

Yun-Gu Kang, Jiwon Choi, Jun-Yeong Lee, Taek-Keun Oh, Jwakyung Sung

Abstract read
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

5 authors.

Yun-Gu KangDepartment of Agricultural Chemistry, Chungnam National University, Daejeon, 34134, South Korea.
Jiwon ChoiDepartment of Agricultural Chemistry, Chungnam National University, Daejeon, 34134, South Korea.
Jun-Yeong LeeDepartment of Agricultural Chemistry, Chungnam National University, Daejeon, 34134, South Korea.
Taek-Keun OhDepartment of Agricultural Chemistry, Chungnam National University, Daejeon, 34134, South Korea. ok5382@cnu.ac.kr.
Jwakyung SungDepartment of Crop Science, Chungbuk National University, Cheongju, 28644, South Korea. jksung73@chungbuk.ac.kr.

Funding

National Research Foundation of Korea RS-2025-24683621
6 · The paper itself

Abstract

Accurate microplastic (MP) quantification in agricultural soils is critical for environmental risk assessment, yet variability in extraction efficiency remains a significant barrier. This study This study investigated machine learning (ML) algorithms to predict MP percentage yield and develop interval-wise correction factors for improved MP quantification. Soils were spiked with four MP types and subjected to density separation using seven brine solutions (e.g., zinc chloride and sodium iodide; 1.00-1.58 g/cm

Identifiers

PMID42481602
PMCPMC13503928

What OpenQuestion holds

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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.