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Record W2971713607 · doi:10.1111/gcb.14823

Climate warming alters subsoil but not topsoil carbon dynamics in alpine grassland

2019· article· en· W2971713607 on OpenAlexaff
Juan Jia, Zhenjiao Cao, Chengzhu Liu, Zhenhua Zhang, Li Lin, Yiyun Wang, Negar Haghipour, Lukas Wacker, Hongyan Bao, Thorsten Dittmar, Myrna J. Simpson, Huan Yang, Thomas W. Crowther, Timothy I. Eglinton, Jin He, Xiaojuan Feng

Bibliographic record

VenueGlobal Change Biology · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil Carbon and Nitrogen Dynamics
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
FundersNational Natural Science Foundation of China
KeywordsSubsoilTopsoilEnvironmental scienceSoil carbonCyclingGlobal warmingCarbon cycleSoil waterGrasslandEcosystemSoil scienceClimate changeAgronomyEcologyForestryBiology

Abstract

fetched live from OpenAlex

Abstract Subsoil contains more than half of soil organic carbon (SOC) globally and is conventionally assumed to be relatively unresponsive to warming compared to the topsoil. Here, we show substantial changes in carbon allocation and dynamics of the subsoil but not topsoil in the Qinghai‐Tibetan alpine grasslands over 5 years of warming. Specifically, warming enhanced the accumulation of newly synthesized ( 14 C‐enriched) carbon in the subsoil slow‐cycling pool (silt‐clay fraction) but promoted the decomposition of plant‐derived lignin in the fast‐cycling pool (macroaggregates). These changes mirrored an accumulation of lipids and sugars at the expense of lignin in the warmed bulk subsoil, likely associated with shortened soil freezing period and a deepening root system. As warming is accompanied by deepening roots in a wide range of ecosystems, root‐driven accrual of slow‐cycling pool may represent an important and overlooked mechanism for a potential long‐term carbon sink at depth. Moreover, given the contrasting sensitivity of SOC dynamics at varied depths, warming studies focusing only on surface soils may vastly misrepresent shifts in ecosystem carbon storage under 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 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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.997

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.024
GPT teacher head0.242
Teacher spread0.218 · 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

Citations202
Published2019
Admission routes1
Has abstractyes

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