Evidence map›Paper›PMID 34948649›Full record

ArticleInternational journal of environmental research and public health2021

Features Importance Analysis of Diesel Vehicles' NO

Hung-Ta Wen, Jau-Huai Lu, Deng-Siang Jhang

Abstract read
In one paragraph

Article in International journal of environmental research and public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Article
  2. Article
  3. Enhancing COScientific reports · 2025
    Article
  4. Article
  5. Article
  6. Article
  7. Assessment of On-Board and Laboratory Gas Measurement Systems for Future Heavy-Duty Emissions Regulations.International journal of environmental research and public health · 2022
    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

3 authors.

Hung-Ta WenDepartment of Mechanical Engineering, National Chung-Hsing University, Taichung City 402, Taiwan.
Jau-Huai LuDepartment of Mechanical Engineering, National Chung-Hsing University, Taichung City 402, Taiwan.
Deng-Siang JhangTaichung Motor Vehicles Office, Taichung City 432, Taiwan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In order to have an accurate and fast prediction of the artificial intelligence (AI) model, the choice of input features is at least as important as the choice of model. The effect of input features selection on the emission models of light diesel vehicles driven on real roads was investigated in this paper. The gradient boosting regression (GBR) model was used to train and to predict the emissions of nitrogen oxide (NO

Indexed as

Air PollutantsArtificial IntelligenceCarbon DioxideEnvironmental MonitoringGasolineMotor VehiclesVehicle EmissionsAir PollutantsCarbon DioxideGasolineVehicle Emissionsdiesel vehiclefeatures importancegradient boosting regressionNOx and CO2 emissionPEMS

Identifiers

PMID34948649
PMCPMC8700826

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.