Evidence map›Paper›PMID 38853165›Full record

ArticleScientific reports2024

Global soil respiration estimation based on ecological big data and machine learning model.

Jiangnan Liu, Junguo Hu, Haoqi Liu, Kanglai Han

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
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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.

Jiangnan LiuCollege of Chemistry and Materials Engineering, Zhejiang A&F University, Hangzhou, 310000, China.
Junguo HuCollege of Chemistry and Materials Engineering, Zhejiang A&F University, Hangzhou, 310000, China. hujunguo@zafu.edu.cn.
Haoqi LiuCollege of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou, 310000, China.
Kanglai HanCollege of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou, 310000, China.

Funding

the Natural Science Foundation of China 31971493Zhejiang Province Public Welfare Technology Application Research Project LGG20F030001
6 · The paper itself

Abstract

Soil respiration (Rs) represents the greatest carbon dioxide flux from terrestrial ecosystems to the atmosphere. However, its environmental drivers are not fully understood, and there are still significant uncertainties in soil respiration model estimates. This study aimed to estimate the spatial distribution pattern and driving mechanism of global soil respiration by constructing a machine learning model method based on ecological big data. First, we constructed ecological big data containing five categories of 27-dimensional environmental factors. We then used four typical machine learning methods to develop the performance of machine learning models under four training strategies and explored the relationship between soil respiration and environmental factors. Finally, we used the RF machine learning algorithm to estimate the global Rs spatial distribution pattern in 2021, driven by multiple dimensions of environmental factors, and derived the annual soil respiration values. The results showed that RF performed better under the four training strategies, with a coefficient of determination R

Indexed as

Ecological big dataGlobal soil respirationMachine learningRandom forest

Identifiers

PMID38853165
PMCPMC11163009

What OpenQuestion holds

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LicenceCC BY
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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.