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Record W4235422339 · doi:10.1139/cjb-2014-0157

Ecological advantage of leaf heteroblasty in <i>Costus pulverulentus</i> (Costaceae)

2015· article· en· W4235422339 on OpenAlexvenueno aff
J. Antonio Guzmán Q.

Bibliographic record

VenueBotany · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicLeaf Properties and Growth Measurement
Canadian institutionsnot available
Fundersnot available
KeywordsBiologySpecific leaf areaCrown (dentistry)BotanyLeaf sizeShadingLeaf area indexDivergence (linguistics)Plant morphologyHorticulturePhotosynthesis

Abstract

fetched live from OpenAlex

Leaf heteroblasty is a plant phenomenon related to leaf development that describes substantial differences between temporally separated plant stages. This study explores the ecological advantage of leaf heteroblasty in the herb Costus pulverulentus C.Presl and analyzes its possible adaptive value. Heteroblasty was studied using leaf morphology analysis and characterizations of leaf area, specific leaf area (SLA), and leaf divergence angles along the crown. A light capture efficiency index (STAR) was also used by simulating plants with only top- or basal-leaf forms to test its adaptive value. Morphological analysis indicated that C. pulverulentus develop two leaf forms: basal leaves with an obovate form, and top leaves with an oblanceolate form. Mid-crown leaves showed a reduced SLA and angles of divergence, and an increased area compared with top and lower leaves, which may indicate greater space utilization for light acquisition. Plants with top-leaf forms showed greater STAR than plants with basal-leaf forms, likely due to lower self-shading and lower crown density produced by leaf arrangements. Results of this study suggests that changes in leaf size and morphology occur in later stages of plant development as an adaptation to reduce self-shading and crown density which promotes an adaptive advantage by increasing the STAR.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.692
Threshold uncertainty score0.213

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 head0.047
GPT teacher head0.230
Teacher spread0.183 · 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

Citations4
Published2015
Admission routes1
Has abstractyes

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