Evidence map›Paper›PMID 41477496›Full record

ArticleHeliyon2025

A general methodological framework for predicting and assessing heavy metal pollution in paddy soils using machine learning models.

Unurnyam Jugnee, Le Jiao, Sainbayar Dalantai, Lili Huo, Yi An, Bayartungalag Batsaikhan, Undrakhtsetseg Tsogtbaatar, Munguntuul Ulziibaatar, Boldbaatar Natsagdorj

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Heliyon, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

9 authors.

Unurnyam JugneeDivision of Environmental and Natural Resources Management, Institute of Geography and Geoecology, Mongolian Academy of Sciences, Ulaanbaatar, 15170, Mongolia.
Le JiaoAgro-Environmental Protection Institute, Ministry of Agriculture and Rural Affairs, Tianjin, 300191, China.
Sainbayar DalantaiDivision of Environmental and Natural Resources Management, Institute of Geography and Geoecology, Mongolian Academy of Sciences, Ulaanbaatar, 15170, Mongolia.
Lili HuoAgro-Environmental Protection Institute, Ministry of Agriculture and Rural Affairs, Tianjin, 300191, China.
Yi AnAgro-Environmental Protection Institute, Ministry of Agriculture and Rural Affairs, Tianjin, 300191, China.
Bayartungalag BatsaikhanDivision of Environmental and Natural Resources Management, Institute of Geography and Geoecology, Mongolian Academy of Sciences, Ulaanbaatar, 15170, Mongolia.
Undrakhtsetseg TsogtbaatarDivision of Environmental and Natural Resources Management, Institute of Geography and Geoecology, Mongolian Academy of Sciences, Ulaanbaatar, 15170, Mongolia.
Munguntuul UlziibaatarDivision of Environmental and Natural Resources Management, Institute of Geography and Geoecology, Mongolian Academy of Sciences, Ulaanbaatar, 15170, Mongolia.
Boldbaatar NatsagdorjDivision of Environmental and Natural Resources Management, Institute of Geography and Geoecology, Mongolian Academy of Sciences, Ulaanbaatar, 15170, Mongolia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Heavy metal contamination in paddies poses a serious threat to ecological and human health. Current researches about heavy metal pollution mainly focus on source apportionment, while robust and accurate predictions on its spatial distribution and driving mechanisms is still lacking. Herein, we developed a general methodological framework to predict and assess heavy metal pollution of paddies in Hunan province, China, by employing Random Forest (RF), Extra Trees Regressor (ETR), Extreme Gradient Boost Regression (XGBR), and Gradient Boosting Regression Tree (GBRT). Results demonstrated that RF performed superiorly in predicting As (

Indexed as

Heavy metalHunan provinceMachine learningPaddy soilPollution prediction

Identifiers

PMID41477496
PMCPMC11908563

What OpenQuestion holds

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LicenceCC BY-NC
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.