Evidence map›Paper›PMID 35898910›Full record

ArticleFrontiers in microbiology2022

Machine Learning Prediction of Nitrification From Ammonia- and Nitrite-Oxidizer Community Structure.

Conard Lee, Fatemeh Amini, Guiping Hu, Larry J Halverson

Open access · goldAbstract read
In one paragraph

Article in Frontiers in microbiology, 2022. 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, top 92% of its field
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

0 citing papers in PubMed, 1 citations in OpenAlex.

No citing paper in PubMed yet.

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

4 authors at 1 institution in 1 country.

Conard LeeInterdepartmental Microbiology Graduate Program, Iowa State University, Ames, IA, United States.
Fatemeh AminiDepartment of Industrial and Manufacturing Engineering, Iowa State University, Ames, IA, United States.
Guiping HuDepartment of Industrial and Manufacturing Engineering, Iowa State University, Ames, IA, United States.
Larry J HalversonInterdepartmental Microbiology Graduate Program, Iowa State University, Ames, IA, United States.
Iowa State University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Accurately modeling nitrification and understanding the role specific ammonia- or nitrite-oxidizing taxa play in it are of great interest and importance to microbial ecologists. In this study, we applied machine learning to 16S rRNA sequence and nitrification potential data from an experiment examining interactions between cropping systems and rhizosphere on microbial community assembly and nitrogen cycling processes. Given the high dimensionality of microbiome datasets, we only included nitrifers since only a few taxa are capable of ammonia and nitrite oxidation. We compared the performance of linear and nonlinear algorithms with and without qPCR measures of bacterial and archaea ammonia monooxygenase subunit A (

Indexed as

ammonia-oxidizersmachine learningmicrobiomenitrificationnitrifiers

Identifiers

PMID35898910
PMCPMC9309558
OpenAlexW4285012793

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.