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Record W2021487776 · doi:10.4236/ajps.2013.45a010

Influence of Weather Conditions on the Surface Morphology and Wetting Behaviour of Superhydrophobic Quaking Aspen Leaves

2013· article· en· W2021487776 on OpenAlexaff
Jared J. Victor, U. Erb

Bibliographic record

VenueAmerican Journal of Plant Sciences · 2013
Typearticle
Languageen
FieldMaterials Science
TopicSurface Modification and Superhydrophobicity
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsWettingMorphology (biology)WaxScanning electron microscopeMaterials scienceEnvironmental scanning electron microscopeGrowing seasonEnvironmental scienceBotanyComposite materialBiology

Abstract

fetched live from OpenAlex

The effects of different environmental conditions on the wetting properties and surface morphology of surperhydrophobic quaking aspen leaves harvested during the 2011 growth season are examined. During this particular season quaking aspen leaves were not able to retain their superhydrophobic properties and associated surface structure features as they have usually been able to do in other years. Representative scanning electron microscopy images and wetting property measurements of quaking aspen leaf surfaces harvested throughout this season are presented and discussed with the objective of linking weather induced environmental stresses that occurred in 2011 to the sudden and unusual reduction in non-wetting properties and drastic changes in leaf surface structure. Erosion and regeneration rates of leaf wax crystals and the impact that environmental factors can have on these are considered and used to explain the occurrence of these unexpected changes.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.025
GPT teacher head0.263
Teacher spread0.238 · 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 source (direct Gemma or distilled Codex), 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

Citations8
Published2013
Admission routes1
Has abstractyes

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