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Record W4360602464 · doi:10.1111/ele.14201

Meta‐analysis shows forest soil <scp> CO <sub>2</sub> </scp> effluxes are dependent on the disturbance regime and biome type

2023· review· en· W4360602464 on OpenAlexafffund
Oluwabunmi J. Akande, Zilong Ma, Chenyan Huang, Fangliang He, Scott X. Chang

Bibliographic record

VenueEcology Letters · 2023
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicSoil Carbon and Nitrogen Dynamics
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsBiomeDisturbance (geology)EcologyEnvironmental scienceEcosystemAtmospheric sciencesSoil scienceBiologyGeologyGeomorphology

Abstract

fetched live from OpenAlex

Abstract Forest soil CO 2 efflux (FCO 2 ) is a crucial process in global carbon cycling; however, how FCO 2 responds to disturbance regimes in different forest biomes is poorly understood. We quantified the effects of disturbance regimes on FCO 2 across boreal, temperate, tropical and Mediterranean forests based on 1240 observations from 380 studies. Globally, climatic perturbations such as elevated CO 2 concentration, warming and increased precipitation increase FCO 2 by 13% to 25%. FCO 2 is increased by forest conversion to grassland and elevated carbon input by forest management practices but reduced by decreased carbon input, fire and acid rain. Disturbance also changes soil temperature and water content, which in turn affect the direction and magnitude of disturbance influences on FCO 2 . FCO 2 is disturbance‐ and biome‐type dependent and such effects should be incorporated into earth system models to improve the projection of the feedback between the terrestrial C cycle and climate change.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0050.006
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.064
GPT teacher head0.262
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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreReview

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

Citations30
Published2023
Admission routes2
Has abstractyes

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