Evidence map›Paper›PMID 40610473›Full record

ArticleScientific reports2025

Machine learning-based mapping of Acidobacteriota and Planctomycetota using 16 S rRNA gene metabarcoding data across soils in Russia.

E A Ivanova, A R Suleymanov, D A Nikitin, M V Semenov, E V Abakumov

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

5 authors.

E A IvanovaLaboratory of Soil Carbon and Microbial Ecology, Dokuchaev Soil Science Institute, Moscow, Russia. ektrnivanova@gmail.com.
A R SuleymanovDepartment of Applied Ecology, Saint Petersburg State University, Saint-Petersburg, Russia.
D A NikitinLaboratory of Soil Carbon and Microbial Ecology, Dokuchaev Soil Science Institute, Moscow, Russia.
M V SemenovLaboratory of Soil Carbon and Microbial Ecology, Dokuchaev Soil Science Institute, Moscow, Russia.
E V AbakumovDepartment of Applied Ecology, Saint Petersburg State University, Saint-Petersburg, Russia. e.abakumov@spbu.ru.

Funding

Russian Science Support Foundation 24-44-00006
6 · The paper itself

Abstract

The soil microbiome plays a crucial role in maintaining healthy ecosystems and supporting sustainable agriculture. Studying its biogeographical structure and distribution is essential for understanding the rates and mechanisms of microbially mediated soil ecosystem services. This study aimed to investigate the spatial distribution patterns of Acidobacteriota and Planctomycetota across soils in Russia, summarizing data from 16S rRNA gene metabarcoding of topsoils. A machine learning approach (Random Forest) was employed to generate digital distribution maps using climatic, topographic, vegetation, geological, and soil variables. Model interpration was performed using variable importance assessment and Shapley values. According to the error metrics, the Acidobacteriota model achieved a root mean squared error (RMSE) of 6.67% and an R

Indexed as

AcidobacteriaDNA Barcoding, TaxonomicMachine LearningRNA, Ribosomal, 16SSoil MicrobiologyEcosystemMicrobiotaRussiaSoilRNA, Ribosomal, 16SSoilAmplicon sequencingEdaphic factorsRandom ForestSoil microbial taxaSpatial abundance patterns

Identifiers

PMID40610473
PMCPMC12229483

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.