Evidence map›Paper›PMID 40593948›Full record

ArticleScientific data2025

Machine learning-enhanced monitoring of global copper mining areas.

Houxuan Li, Peng Wang, Tim T Werner, Bin Chen, Wei-Qiang Chen

Erratum issuedAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. 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

5 · Who and what money

Authors and funding

5 authors.

Houxuan LiInstitute of Urban Environment, Chinese Academy of Sciences, 1799 Jimei Road, Xiamen, 361021, China.
Peng WangInstitute of Urban Environment, Chinese Academy of Sciences, 1799 Jimei Road, Xiamen, 361021, China. pwang@iue.ac.cn.ORCID http://orcid.org/0000-0001-7170-1494
Tim T WernerSchool of Geography, University of Melbourne, 221 Bouverie Street, Carlton, VIC, 3010, Australia.
Bin ChenDepartment of Environment Science and Technology, Fudan University, 220 Handan Rd., Shanghai, 200433, China.ORCID http://orcid.org/0000-0001-8326-4551
Wei-Qiang ChenInstitute of Urban Environment, Chinese Academy of Sciences, 1799 Jimei Road, Xiamen, 361021, China.ORCID http://orcid.org/0000-0002-7686-2331

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Copper is one of the most critical minerals for the global transition to low-carbon energy. However, as copper mining activities expand worldwide, they often result in significant environmental impacts, yet the monitoring approaches and up-to-date databases remain limited. In this study, we present a high-resolution, site-specific database of global copper mining activities, developed using a machine learning approach that leverages Earth observation images and various dispersed data sources. Our database encompasses approximately 1,313 copper mines, covering an area of 7,267 km

Identifiers

PMID40593948
PMCPMC12217162

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.