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Record W3145186477

Relationship between flower size and leaf size,number of Stellera chamaejasme population of degraded alpine grassland along an altitude gradient

2015· article· en· W3145186477 on OpenAlexaff
Zhang Qia

Bibliographic record

VenueShengtaixue zazhi · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsScience North
Fundersnot available
KeywordsAltitude (triangle)Biomass (ecology)GrasslandBiologyLeaf sizeSeedlingPopulationSpecific leaf areaBotanyHorticultureAgronomyPhotosynthesisMathematics
DOInot available

Abstract

fetched live from OpenAlex

The relationship between flower size and leaf size and number reflects the plant adaptation strategies in external morphology during the long-term interaction of plants with different environments,and the variation of the relationship reflects plant adaptation to heterogeneous environments. In this study,an investigation was carried out to examine the relationship between flower size and leaf size and number of Stellera chamaejasme along four different altitude gradients in an alpine grassland in the northern slope of Qilian Mountains. The results showed that,with increasing elevation,the height,density,and aboveground biomass of the plant communities displayed a pattern of initial increase and then a decline. The aboveground biomass,plant height and leaf biomass of S. chamaejasme declined gradually,while reproductive allocation,flower size and leaf number both increased gradually. Flower size of S. chamaejasme was significantly positively correlated with leaf number( P 0. 01),but negatively with leaf biomass,while there was no significant correlation between flower size and leaf biomass( P 0. 05). Therefore,habitat had a significant influence on the dependency among size of flower and leaf size and number. The plant size would be decreased by the environmental stress at high altitude,and both flower size and leaf number are increased while the leaf size is declined to ensure the reproductive success of S. chamaejasme.

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)0.001
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 score0.007
Threshold uncertainty score0.562

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.031
GPT teacher head0.280
Teacher spread0.249 · 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

Citations3
Published2015
Admission routes1
Has abstractyes

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