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Mechanical Character of Typica1 Plant Leaf Surfaces

2010· article· en· W1596559010 on OpenAlexvenueno aff
Shujie Wang, Luquan Ren, Yan Liu, Yue Yang

Bibliographic record

VenueAdvances in natural science/Advances in natural sciences · 2010
Typearticle
Languageen
FieldMaterials Science
TopicSurface Modification and Superhydrophobicity
Canadian institutionsnot available
Fundersnot available
KeywordsNelumbo nuciferaPhyllostachysBambusaMorphology (biology)Character (mathematics)Materials scienceCannaBotanyBambooComposite materialHorticultureBiologyMathematicsGeometryFood science

Abstract

fetched live from OpenAlex

The hardness of typical plant leaves surfaces was investigated and analyzed by nano-hardness tester. Results show that the mechanical character of varied texture and varied surface morphologies shows regularity. The hardness of coriaceous fresh leaves (Bambusa phyllostachys pubescens etc.) and ligneous leaves is great. Waxiness leaves′ hardness (such as Nelumbo nucifera Gaertn, Canna indica Linn (generalis) is little. The hardness of convex morphology part is better than that of concave morphology part on non-smooth morphology leaf surfaces. And that the hardness of surface layer is better than that of internal layer in the same leaf. This study may be important biological foundation for design and fabrication of bionic engineering surface and composite materials. Key words: engineering bionic; plant leaf; hardness; mechanical character

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.052
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.004
Science and technology studies0.0010.005
Scholarly communication0.0000.007
Open science0.0030.000
Research integrity0.0000.001
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 head0.011
GPT teacher head0.297
Teacher spread0.285 · 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.

Study designBench or experimental
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

Citations0
Published2010
Admission routes1
Has abstractyes

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