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Record W4213075133 · doi:10.1002/eap.2575

Ecosystem restoration and belowground multifunctionality: A network view

2022· article· en· W4213075133 on OpenAlexaff
Dashuan Tian, Yangzhou Xiang, Eric W. Seabloom, Han Y. H. Chen, Jinsong Wang, Guirui Yu, Ye Deng, Zhaolei Li, Shuli Niu

Bibliographic record

VenueEcological Applications · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil Carbon and Nitrogen Dynamics
Canadian institutionsLakehead University
FundersYouth Innovation Promotion Association of the Chinese Academy of SciencesNational Natural Science Foundation of China
KeywordsRestoration ecologyEcosystemEnvironmental scienceEcologyKey (lock)Land degradationEcosystem servicesLand useBiology

Abstract

fetched live from OpenAlex

Ecological restoration is essential to reverse land degradation worldwide. Most studies have assessed the restoration of ecosystem functions individually, as opposed to a holistic view. Here we developed a network-based ecosystem multifunctionality (EMF) framework to identify key functions in evaluating EMF restoration. Through synthesizing 293 restoration studies (2900 observations) following cropland abandonment, we found that individual soil functions played different roles in determining the restoration of belowground EMF. Soil carbon, total nitrogen, and phosphatase were key functions to predict the recovery of belowground EMF. On average, abandoned cropland recovered ~19% of EMF during 18 years. The restoration of EMF became larger with longer recovery time and higher humidity index, but lower with increasing soil depth and initial soil carbon. Overall, this study presents a network-based EMF framework, effectively helping to evaluate the success of ecosystem restoration and identify the key functions.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.512
Threshold uncertainty score0.774

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.019
GPT teacher head0.218
Teacher spread0.198 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations27
Published2022
Admission routes1
Has abstractyes

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