MétaCan
Menu
Back to cohort
Record W850212 · doi:10.1530/jrf.0.0490219

Using bio-based materials in the automotive industry

2013· article· en· W850212 on OpenAlexafffund
Simon Che Wen Tseng

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing and 3D Printing Technologies
Canadian institutionsUniversity of Windsor
FundersUniversity of Windsor
KeywordsAutomotive industryManufacturing engineeringBusinessEngineering

Abstract

fetched live from OpenAlex

The objective of this research is to evaluate the environmental performance of polylactic acid (PLA) + flax fiber bio-composite against the current in production composite of polypropylene (PP) + wood dust via life cycle analysis (LCA). The system boundary is an extended gate-to-gate LCA that includes the materials production process. In order to complete the LCA set forth, a necessary iterative process of dataset matching was done to convert NatureWorks LLC's Ingeo (PLA) dataset from the USLCI database to the GaBi database in order to model the LCA conducted. The bio-composite of PLA + flax produces less greenhouse gas emissions that contribute to global warming potentials (GWP) largely due to the carbon sequestration of corn production. The current in production composite of PP + wood dust contributes less to both acidification potential (AP) of seawater and photochemical ozone creation potential (POCP) largely due to less agricultural processes. The polymer resin production process is the primary parameter for energy consumption in both composites.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0050.004

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 head0.034
GPT teacher head0.244
Teacher spread0.210 · 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 designNot applicable
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

Citations3
Published2013
Admission routes2
Has abstractyes

Explore more

Same topicAdditive Manufacturing and 3D Printing TechnologiesFrench-language works237,207