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Record W2575612550 · doi:10.1080/00103624.2016.1269802

Effects of Inorganic and Organic Fertilizers on Soil CO<sub>2</sub> Efflux and Labile Organic Carbon Pools in an Intensively Managed Moso Bamboo (<i>Phyllostachys pubescens</i>) Plantation in Subtropical China

2017· article· en· W2575612550 on OpenAlexaff
Meng Yang, Yongfu Li, Scott X. Chang, Tian Yue, Weijun Fu, Peikun Jiang, Guomo Zhou

Bibliographic record

VenueCommunications in Soil Science and Plant Analysis · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil Carbon and Nitrogen Dynamics
Canadian institutionsUniversity of Alberta
FundersNatural Science Foundation of Zhejiang ProvinceNational Natural Science Foundation of China
KeywordsBambooSoil carbonChemistryAgronomyEnvironmental chemistryFertilizerTotal organic carbonSoil organic matterEffluxEnvironmental sciencePhyllostachysNutrientSoil waterBotanySoil scienceBiology

Abstract

fetched live from OpenAlex

Impact of combined application of inorganic and organic fertilizers on soil carbon dioxide (CO2) emission is poorly understood. We investigated the effects of inorganic fertilizer (IF), organic fertilizer (OF), and a mixture of organic and inorganic fertilizers (OIF) applications on the dynamics of soil CO2 efflux in intensively managed Moso bamboo plantations. Soil CO2 efflux and concentrations of water soluble organic C (WSOC) and microbial biomass C (MBC) in the IF treatment were higher than those in the control but lower than those in the OF and OIF treatments. Both OF and OIF treatments increased the SOC stock. Strong exponential relationships (p < 0.01) between soil temperature and CO2 efflux were observed in all treatments. Soil CO2 efflux in all four treatments was correlated with WSOC (p < 0.05) but not with MBC. We concluded the combined approach can possibly contribute to increasing the level of SOC stock in intensively managed plantations.

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)0.001
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 score0.366
Threshold uncertainty score0.920

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.015
GPT teacher head0.243
Teacher spread0.228 · 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

Citations28
Published2017
Admission routes1
Has abstractyes

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