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Record W2616922573

BIOPRODUCTS OF AUTOMOTIVE ACCESSORIES: RETHINKING DESIGN MATERIALS THROUGH CORNSTARCH, SUGARCANE AND HEMP

2007· article· en· W2616922573 on OpenAlexaboutno aff
Apisak Sindhuphak

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicDesign Education and Practice
Canadian institutionsnot available
Fundersnot available
KeywordsAutomotive industryBioproductsEngineeringPolylactic acidRenewable energyManufacturing engineeringWaste managementMaterials scienceBiofuelComposite material
DOInot available

Abstract

fetched live from OpenAlex

Current bioproducts or bio-based products do not only require less energy to produce than petroleum-based products, they are made with renewable sources that engineered from excessive waste and natural local materials. This paper identifies alternative design solutions by suggesting a use of natural materials such as cornstarch, sugarcanes and hemp in designing automotive accessories. Leading automotive industries have focused on using bio-based materials for possible vehicle details such as dashboard panels, finishing trims and optional features and casing for light covers. A fermentation broth derived from cornstarch and sugarcanes, which were recovered as Polylactic acid or Polylactide (PLA), were selected by designer and automotive engineer to reconstruct bio-based materials to improve identity of bio-based design for an automotive world. This choice of material process yields similar quality to materials made from thermoplastic or materials categorized as lightweight-metal. Additional design examples of bio-based materials are products made from hemp fiber for bus seat in Canada, and biodegradable phone casing from England and Japan. These examples are described as solutions, which show sustainable use of alternative materials and suggest design applications that reflect concerns for the environment.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.258
Threshold uncertainty score0.539

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.048
GPT teacher head0.290
Teacher spread0.242 · 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 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

Citations6
Published2007
Admission routes1
Has abstractyes

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