In one paragraphArticle in Environmental science & technology, 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
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 registryThe 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 literatureWho cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
4 · The recordCorrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
5 · Who and what moneyAuthors and funding
14 authors.
Christine T CowieWoolcock Institute of Medical Research, Macquarie University, Macquarie Park, New South Wales 2113, Australia.ORCID 0000-0003-1177-4733 Ivan C HaniganSchool of Population Health, Curtin University, Perth, Western Australia 6845, Australia.
Wenhua YuSchool of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria 3004, Australia.ORCID 0000-0002-4733-8610 Cassandra YuenSchool of Population Health, Curtin University, Perth, Western Australia 6845, Australia.ORCID 0000-0001-5433-6018 Karthik GopiSydney School of Public Health, and University Centre for Rural Health, The University of Sydney, Sydney, New South Wales 2050, Australia.
Geoffrey G MorganSydney School of Public Health, and University Centre for Rural Health, The University of Sydney, Sydney, New South Wales 2050, Australia.ORCID 0000-0003-4046-2405 Nicolas Borchers-ArriagadaMenzies Institute for Medical Research, University of Tasmania, Hobart, Tasmania 7000, Australia.
Jane HeyworthThe School of Population and Global Health, The University of Western Australia, Crawley, Perth, Western Australia 6009, Australia.
Martin CopeCommonwealth Scientific, Industrial & Research Organisation (CSIRO), Aspendale, Victoria 3195, Australia.
Lidia MorawskaInternational Laboratory for Air Quality and Heath (ILAQH), School of Earth & Atmospheric Sciences, Queensland University of Technology, Brisbane, Queensland 4000, Australia.ORCID 0000-0002-0594-9683 Bin JalaludinSchool of Population Health, University of NSW, Sydney, New South Wales 2052, Australia.
Guy B MarksWoolcock Institute of Medical Research, Macquarie University, Macquarie Park, New South Wales 2113, Australia.
Yuming GuoSchool of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria 3004, Australia.
Luke D KnibbsPublic Health Research Analytics and Methods for Evidence, Sydney Local Health District, Camperdown, New South Wales 1450, Australia.ORCID 0000-0002-0399-2370 Funding
No grant is acknowledged in the PubMed record.
6 · The paper itselfAbstract
Recent studies report improved performance of ensemble models over machine learning (ML) models for air pollution estimation, although there is little evidence of their added value in settings with sparsely monitored data. We developed and compared three ML models, a supervised linear regression (SLR) model, and an ensemble model, for estimating annual average PM
Indexed as
Machine LearningParticulate MatterAir PollutantsAir PollutionBoosting Machine Learning AlgorithmsEnvironmental MonitoringNitrogen DioxideRandom ForestAir PollutantsNitrogen DioxideParticulate Matterair pollutionensemble modelingexposuremachine learningnitrogen dioxideparticulate matter
Identifiers
PMID42378067
PMCPMC13374088
What OpenQuestion holds
Textmetadata
LicenceCC BY
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