Evidence map›Paper›PMID 39188328›Full record

ArticleWater research X2024

High resolution data visualization and machine learning prediction of free chlorine residual in a green building water system.

S Wei, R Richard, D Hogue, I Mondal, T Xu, T H Boyer, K A Hamilton

Abstract read
In one paragraph

Article in Water research X, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Observational
  2. Article
  3. Review
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

7 authors.

S WeiSchool of Sustainable Engineering and the Built Environment, Arizona State University, Tempe, AZ 85281, United States.
R RichardWilson & Company Engineers, United States.
D HogueSchool of Sustainable Engineering and the Built Environment, Arizona State University, Tempe, AZ 85281, United States.
I MondalSchool of Sustainable Engineering and the Built Environment, Arizona State University, Tempe, AZ 85281, United States.
T XuSchool of Sustainable Engineering and the Built Environment, Arizona State University, Tempe, AZ 85281, United States.
T H BoyerSchool of Sustainable Engineering and the Built Environment, Arizona State University, Tempe, AZ 85281, United States.
K A HamiltonSchool of Sustainable Engineering and the Built Environment, Arizona State University, Tempe, AZ 85281, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

People spend most of their time indoors and are exposed to numerous contaminants in the built environment. Water management plans implemented in buildings are designed to manage the risks of preventable diseases caused by drinking water contaminants such as opportunistic pathogens (e.g.,

Indexed as

Artificial intelligence (AI)Machine learning (ML)Opportunistic pathogensPremise plumbing

Identifiers

PMID39188328
PMCPMC11345929

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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 Socratic graph.