Machine learning methods to predict cadmium (Cd) concentration in rice grain and support soil management at a regional scale.

Bo-Yang Huang, Qi-Xin Lü, Zhi-Xian Tang, Zhong Tang, Hong-Ping Chen, Xin-Ping Yang, Fang-Jie Zhao, Peng Wang

Abstract read

In one paragraph

Article in Fundamental research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it

0cells of the map it votes in

3citing 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

3 citing papers in PubMed.

Artificial intelligence in food safety.NPJ science of food · 2026
Review
Interpretable machine learning models to predict cadmium in wheat for safe production and soil management.Fundamental research · 2026
Article
A general methodological framework for predicting and assessing heavy metal pollution in paddy soils using machine learning models.Heliyon · 2025
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

8 authors.

Bo-Yang HuangJiangsu Collaborative Innovation Center for Solid Organic Waste Resource Utilization, College of Resources and Environmental Sciences, Nanjing Agricultural University, Nanjing 210095, China.

Qi-Xin LüJiangsu Collaborative Innovation Center for Solid Organic Waste Resource Utilization, College of Resources and Environmental Sciences, Nanjing Agricultural University, Nanjing 210095, China.

Zhi-Xian TangJiangsu Collaborative Innovation Center for Solid Organic Waste Resource Utilization, College of Resources and Environmental Sciences, Nanjing Agricultural University, Nanjing 210095, China.

Zhong TangJiangsu Collaborative Innovation Center for Solid Organic Waste Resource Utilization, College of Resources and Environmental Sciences, Nanjing Agricultural University, Nanjing 210095, China.

Hong-Ping ChenJiangsu Collaborative Innovation Center for Solid Organic Waste Resource Utilization, College of Resources and Environmental Sciences, Nanjing Agricultural University, Nanjing 210095, China.

Xin-Ping YangJiangsu Collaborative Innovation Center for Solid Organic Waste Resource Utilization, College of Resources and Environmental Sciences, Nanjing Agricultural University, Nanjing 210095, China.

Fang-Jie ZhaoJiangsu Collaborative Innovation Center for Solid Organic Waste Resource Utilization, College of Resources and Environmental Sciences, Nanjing Agricultural University, Nanjing 210095, China.

Peng WangJiangsu Collaborative Innovation Center for Solid Organic Waste Resource Utilization, College of Resources and Environmental Sciences, Nanjing Agricultural University, Nanjing 210095, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Rice is a major dietary source of the toxic metal cadmium (Cd). Concentration of Cd in rice grain varies widely at the regional scale, and it is challenging to predict grain Cd concentration using soil properties. The lack of reliable predictive models hampers management of contaminated soils. Here, we conducted a three-year survey of 601 pairs of soil and rice samples at a regional scale. Approximately 78.3% of the soil samples exceeded the soil screening values for Cd in China, and 53.9% of rice grain samples exceeded the Chinese maximum permissible limit for Cd. Predictive models were developed using multiple linear regression and machine learning methods. The correlations between rice grain Cd and soil total Cd concentrations were poor (

Indexed as

CadmiumFood safetyHeave metalsMachine learningPredictive modelSoil contamination

Identifiers

PMID39431142

DOI10.1016/j.fmre.2023.02.016

PMCPMC11489518

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LicenceCC BY

Read underepoch 390

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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.