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Record W6998694008

Användares erfarenheter av däckslitage hos elfordon : en enkät- och intervjustudie

2022· article· en· W6998694008 on OpenAlexaboutno aff

Bibliographic record

VenueKTH Publication Database DiVA (KTH Royal Institute of Technology) · 2022
Typearticle
Languageen
FieldEngineering
TopicElectric Vehicles and Infrastructure
Canadian institutionsnot available
Fundersnot available
KeywordsElectrificationBrakeRentingElectric vehicleQuarter (Canadian coin)Electric carsInternal combustion engine
DOInot available

Abstract

fetched live from OpenAlex

Electrification of vehicles is considered as a solution to reduce climate gas emissions as well as locally emitted air pollution components due to zero exhaust emissions. Also brake wear emissions are expected to be reduced due to the use of regenerative2 braking. However, electric vehicles (EVs) have higher and more direct torque and can therefore accelerate fast. They are also generally heavier than equivalent internal combustion engine vehicles (ICEVs). These properties are hypothesized to lead to higher non-exhaust emissions from tyre and road wear as well as higher resuspension of road dust. On the other hand, driving behaviour in EVs might differ due to e.g. driving range issues. This study aims at investigating users’ experiences with tyre wear of EVs, Plug-in Hybrid Electric Vehicles (PHEVs) and Hybrid Electric Vehicles (HEVs). The study was done using web-based inquiries and interviews. Two formats of surveys, one for private users and one for professional users were prepared. The professional survey included taxi, bus transport and car rental companies. The survey to private owners was communicated to the public using an ad on Facebook and the survey to professional users was sent by emails to companies. Furthermore, some interviews were done by professional users. 307 users answered the survey to private users and 28 companies answered the survey of professional users. Furthermore, six representatives for companies were interviewed. The results showed that approximately 33% of private users and 12.5% of professional users experienced faster tyre wear in their EVs/HEVs/PHEVs, compared with tyre wear in ICEVs. Generally, for all electric vehicle types, most professional users experience similar tyre wear as for ICEVs. Vehicle acceleration and weight are the two most commonly mentioned reasons for faster tyre wear, while driving behaviour is the most commonly answered reason for slower tyre wear, compared to tyre wear in ICEVs

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)0.004
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.058
Threshold uncertainty score0.195

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0080.004
Open science0.0010.005
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0580.032

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 head0.009
GPT teacher head0.224
Teacher spread0.215 · 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 designQualitative
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

Citations0
Published2022
Admission routes1
Has abstractyes

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