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Skateboard deck materials selection

2018· article· en· W2791651064 on OpenAlexafffund
Liu Haoyu, Tasha Coote, Aiolos, Charlie Charlie

Bibliographic record

VenueIOP Conference Series Earth and Environmental Science · 2018
Typearticle
Languageen
FieldEngineering
TopicTransportation Safety and Impact Analysis
Canadian institutionsUniversity of British Columbia
FundersUniversity of British Columbia
KeywordsMaterial selectionMaterials scienceFracture toughnessComposite materialBrittlenessAlloyDeckToughnessMaterial propertiesTitaniumBrittle fractureFracture (geology)Titanium alloyStructural engineeringMetallurgyEngineering

Abstract

fetched live from OpenAlex

The goal of this project was to identify the ideal material for a skateboard deck under $200 in price, minimizing the weight. The material must have a fracture toughness of 5 MPa/m2, have a minimum lifetime of 10, 000 cycles and must not experience brittle fracture. Both single material and hybrid solutions were explored. When further selecting to minimize weight, woods were found to be the best material. Titanium alloy-wood composites were explored to determine the optimal percentage composition of each material.A sandwich panel hybrid of 50% titanium alloy and 50% wood (Ti-Wood) was found to be the optimum material, performing better than the currently used plywood.

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 categoriesInsufficient payload (model declined to judge)
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 score0.298
Threshold uncertainty score1.000

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 studies0.0000.001
Scholarly communication0.0000.001
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 head0.008
GPT teacher head0.193
Teacher spread0.185 · 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

Citations4
Published2018
Admission routes2
Has abstractyes

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