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Record W4309455652 · doi:10.3233/atde220824

Application of New Plastic Materials in New Energy Vehicle Design

2022· book-chapter· en· W4309455652 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAdvances in transdisciplinary engineering · 2022
Typebook-chapter
Languageen
FieldEngineering
TopicMechanical Engineering and Vibrations Research
Canadian institutionsUniversity of British Columbia
FundersDepartment of Science and Technology of Liaoning Province
KeywordsAutomotive industryNew energyPlastics industryManufacturing engineeringEngineeringMechanical engineeringProcess engineeringConstruction engineeringMaterials scienceComposite materialAerospace engineering

Abstract

fetched live from OpenAlex

With the increasing output of new energy vehicles, higher requirements are put forward for automotive plastics. On the basis of accurately understanding the advantages and applicable conditions of various new plastic materials, according to relevant standards, it is of great significance for the development of new energy vehicle manufacturing industry to design and apply them to new energy vehicles. Taking bio-plastics and carbon fiber reinforced plastics as examples, based on the analysis of the physical and chemical characteristics of new plastic materials, it discusses the application status and prospect of new plastic materials in the design of new energy vehicles. Future development direction of new plastics refers to develop new plastics and expand their application types through more innovative research and development of new plastics modification technology with its performance improvement so as to be applied in more structural parts in new energy vehicles.

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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.945
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
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 head0.015
GPT teacher head0.244
Teacher spread0.229 · 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