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Record W1989810908 · doi:10.1139/b09-095

Allometry of <i>Corispermum macrocarpum</i> in response to soil nutrient, water, and population density

2010· article· en· W1989810908 on OpenAlexvenueno aff
Yingxin Huang, Xueyong Zhao, Daowei Zhou, Yayong Luo, Wei Mao

Bibliographic record

VenueBotany · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsnot available
Fundersnot available
KeywordsBiologyAllometryNutrientBiomass (ecology)PlasticityPhenotypic plasticityPopulationPopulation densityAgronomyBotanyEcology

Abstract

fetched live from OpenAlex

The allometric effects of Corispermum macrocarpum Bunge in response to soil nutrient content, water content, and population density were compared in a greenhouse experiment. The results showed that biomass allocation was size-dependent. The plasticity of roots, leaf allocation, and reproductive effort was “true” plasticity (i.e., changing biomass allocation in response to environmental conditions regardless of plant size), and no plasticity was detected in stem allocation in response to soil nutrient variation. At a low soil nutrient content, C. macrocarpum allocated more resources to reproductive organs than to roots and leaves at equivalent plant size, but the value of root allocation was consistent because of the trade-off between the effects of plant size and soil nutrient content. In the response to soil water variation, the plasticity of root allocation and reproductive effort was true plasticity, the plasticity of stem allocation was “apparent” plasticity (i.e., changing biomass allocation in response to plant size regardless of environmental conditions), which was caused by plant size, but there was no plasticity in leaf allocation. Except for the true plasticity of root allocation, there was no plasticity in stems, leaves, and reproductive organs in response to population density.

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 score0.255
Threshold uncertainty score0.377

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 head0.005
GPT teacher head0.223
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

Citations11
Published2010
Admission routes1
Has abstractyes

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