Evidence map›Paper›PMID 42473662›Full record

ArticleiScience2026

Drivers and distribution of soil arsenic in China's yellow river irrigation area by machine learning.

Jinghao Guo, Tiantian Ma, Rongguang Shi, Yilong Yu, Ke Yang, Xiangyu Liang, Junhua Ma

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in iScience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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.

Jinghao GuoAgro-Environmental Protection Institute, Ministry of Agriculture and Rural Affairs, Chinese Academy of Agricultural Sciences, Tianjin 300170, China.
Tiantian MaAgro-Environmental Protection Institute, Ministry of Agriculture and Rural Affairs, Chinese Academy of Agricultural Sciences, Tianjin 300170, China.
Rongguang ShiAgro-Environmental Protection Institute, Ministry of Agriculture and Rural Affairs, Chinese Academy of Agricultural Sciences, Tianjin 300170, China.
Yilong YuAgro-Environmental Protection Institute, Ministry of Agriculture and Rural Affairs, Chinese Academy of Agricultural Sciences, Tianjin 300170, China.
Ke YangAgro-Environmental Protection Institute, Ministry of Agriculture and Rural Affairs, Chinese Academy of Agricultural Sciences, Tianjin 300170, China.
Xiangyu LiangAgro-Environmental Protection Institute, Ministry of Agriculture and Rural Affairs, Chinese Academy of Agricultural Sciences, Tianjin 300170, China.
Junhua MaAgricultural Environmental Protection Monitoring Station of Ningxia Hui Autonomous Region, Ningxia 750000, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Arsenic contamination in irrigated agricultural soils poses global health and ecosystem risks. Using China's Yellow River Irrigation District as a case study, we developed an interpretable machine learning framework to predict soil arsenic distribution and identify its driving mechanisms. Among five models (XGBoost, RF, SVM, MLP, and MLR), XGBoost achieved the highest accuracy (R

Indexed as

Heavy metalsLISAMachine learningSoil arsenicSpatial distribution prediction

Identifiers

PMID42473662
PMCPMC13380747

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.