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Record W2599747470 · doi:10.1111/ele.12767

Influence of multiple global change drivers on terrestrial carbon storage: additive effects are common

2017· review· en· W2599747470 on OpenAlexafffund
Kai Yue, Dario Fornara, Wanqin Yang, Yan Peng, Changhui Peng, Zelin Liu, Fuzhong Wu

Bibliographic record

VenueEcology Letters · 2017
Typereview
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsUniversité du Québec à Montréal
FundersChina Scholarship CouncilNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsTerrestrial ecosystemTerrestrial plantBiomass (ecology)Plant communityGlobal changeEcosystemEcologyEnvironmental scienceClimate changeStorage effectInteractionCarbon cycleGlobal warmingBiologyAtmospheric sciencesAgronomySpecies richnessCompetition (biology)

Abstract

fetched live from OpenAlex

Abstract The interactive effects of multiple global change drivers on terrestrial carbon (C) storage remain poorly understood. Here, we synthesise data from 633 published studies to show how the interactive effects of multiple drivers are generally additive (i.e. not differing from the sum of their individual effects) rather than synergistic or antagonistic. We further show that (1) elevated CO 2 , warming, N addition, P addition and increased rainfall, all exerted positive individual effects on plant C pools at both single‐plant and plant‐community levels; (2) plant C pool responses to individual or combined effects of multiple drivers are seldom scale‐dependent (i.e. not differing from single‐plant to plant‐community levels) and (3) soil and microbial biomass C pools are significantly less sensitive than plant C pools to individual or combined effects. We provide a quantitative basis for integrating additive effects of multiple global change drivers into future assessments of the C storage ability of terrestrial ecosystems.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.817
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.022
GPT teacher head0.268
Teacher spread0.247 · 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.

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

Citations249
Published2017
Admission routes2
Has abstractyes

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