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Record W2371592974

Spatial distribution patterns of specific root lengths of Avena sativa L. and Vicia villosa Roth in mixed-sowing grassland under density dependence in northern slope of Qilian Mountains

2015· article· en· W2371592974 on OpenAlexaff
Song Qing-hu

Bibliographic record

VenueShengtaixue zazhi · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil erosion and sediment transport
Canadian institutionsScience North
Fundersnot available
KeywordsSowingAvenaVicia sativaGrasslandVicia villosaAgronomyVillosaBiomass (ecology)Spatial distributionBiologyBotanyMathematics
DOInot available

Abstract

fetched live from OpenAlex

The specific root length( SRL) reflects the feature of plants' adaptation to different environments. In this study,the mixed-sowing grassland of gramineous grass Avena sativa and leguminous grass Vicia vilosa with the density ratio of CK1( 1∶ 0),A1( 8 ∶ 2),A2( 6 ∶ 4),A3( 5∶ 5),A4( 4∶ 6),A5( 2∶ 8) and CK2( 0∶ 1) was established in the upper reaches of Qilian Mountains,Gansu Province,Northwest China in 2013,aimed to study the spatial distribution patterns of SRLs of the two mixed-sowing grasses. The results showed that the total root length and SRL of all mixed sowing groups of A. sativa were greater than those of V. villosa( P 0.05),but both species had no significant difference in underground biomass. With the decrease of A. sativa density and the increase of V. vilosa density in mixed-sowing grassland,the root lengths of both grasses increased gradually( P 0. 05),the root biomass decreased first and then increased( P 0. 05),the SRLs increased first and then decreased( P 0. 05). The SRLs of both species in the five mixed sowing groups gradually decreased with soil layers,and the superficial degree in fine roots of A. sativa was higher than that of V. villosa. The layering distribution of roots of current-year grass changed the resource allocation strategy,gradually optimized the utilization structure of resources,and maximized the utilization of soil resources.

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 score0.241
Threshold uncertainty score0.998

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 head0.019
GPT teacher head0.213
Teacher spread0.194 · 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

Citations1
Published2015
Admission routes1
Has abstractyes

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