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Record W2624977389 · doi:10.3390/su9061019

Effects of Understory Liana Trachelospermum jasminoides on Distributions of Litterfall and Soil Organic Carbon in an Oak Forest in Central China

2017· article· en· W2624977389 on OpenAlexaff
Qing Shang, Bo Zhang, Kesheng Zhang, Junwei Luan

Bibliographic record

VenueSustainability · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsUniversity of New Brunswick
FundersChina Postdoctoral Science Foundation
KeywordsLianaUnderstoryEnvironmental sciencePlant litterSoil carbonEcosystemLitterForest ecologyTerrestrial ecosystemSoil respirationSoil organic matterAgroforestryEcologyBiologySoil waterSoil scienceCanopy

Abstract

fetched live from OpenAlex

Liana constitutes an important structural and functional component in many forest ecosystems and has profound impacts on forest carbon (C) cycling. However, whether and how liana regulates spatial distributions of litterfall and soil organic C are still poorly understood. To address this critical knowledge gap, we investigated litterfall composition and soil physicochemical characteristics in stands with different densities of liana (Trachelospermum jasminoides (Lindl.) Lem.). Both fresh and decomposed leaf litters were greater in the stands with high density of the liana species T. jasminoides. More liana covered stands also had higher soil respiration rate, soil organic C, and total nitrogen than those with less liana. The findings demonstrate that understory liana can regulate litterfall distribution and thus soil organic C, suggesting that the influences of understory liana on belowground ecological processes should be considered while assessing the role of liana in forest 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 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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.023
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

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 head0.005
GPT teacher head0.232
Teacher spread0.227 · 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 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

Citations2
Published2017
Admission routes1
Has abstractyes

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