ReviewEnvironmental science and ecotechnology2024
Deep-learning architecture for PM
Review in Environmental science and ecotechnology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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.
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.
Who cites it
8 citing papers in PubMed.
- PM2.5/PM10 Forecasting System with Benchmarking of 44 Machine Learning Algorithms and Ensemble Learning Approaches.Sensors (Basel, Switzerland) · 2026Article
- Forecasting of PMScientific reports · 2026Article
- Evaluating deep learning time series models for PMiScience · 2026Article
- Ambient PMCurrent environmental health reports · 2026Review
- A novel deep learning prediction method for PM2.5 integrating improved MSTL decomposition and FATA optimization algorithm.PloS one · 2026Article
- High-Resolution Estimation of Daily PMRemote sensing · 2025Article
- Performance and generalizability impacts of incorporating location encoders into deep learning for dynamic PMGIScience & remote sensing · 2025Article
- GTAT-GRN: a graph topology-aware attention method with multi-source feature fusion for gene regulatory network inference.Frontiers in genetics · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accurately predicting the concentration of fine particulate matter (PM
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.