MétaCan
Menu
Back to cohort
Record W1847997981 · doi:10.4271/2008-01-0593

Industry Implementation of Automotive Electronic Stability Control (ESC) Systems

2008· article· en· W1847997981 on OpenAlexaff
Nicholas J. Durisek, Kevan J. Granat

Bibliographic record

VenueSAE International journal of passenger cars. Electronic and electrical systems · 2008
Typearticle
Languageen
FieldEngineering
TopicReal-time simulation and control systems
Canadian institutionsDynamic Systems Analysis (Canada)
Fundersnot available
KeywordsAutomotive industryControl (management)Electronic stability controlManufacturing engineeringAutomotive engineeringComputer scienceBusinessEngineeringArtificial intelligenceAerospace engineering

Abstract

fetched live from OpenAlex

The documented availability of electronic stability control (ESC) systems on passenger vehicles is useful in understanding the integration of ESC technology into the North American automobile market. Unfortunately, the sources that document ESC system availability are not always consistent with each other and many show discrepancies with information from the manufacturers. In this study, the history of the implementation of ESC systems in passenger vehicles is reported based on information combined from several different organizations including the National Highway Traffic Safety Administration (NHTSA), the Insurance Institute for Highway Safety (IIHS), Ward's Auto World, and Consumers Reports. Where discrepancies exist between these different sources of data, clarification was gained through further research of information available from the manufacturer, including corporate press releases, owner manuals, and vehicle brochures. ESC system implementation in vehicles as standard equipment is studied in terms of model year sales volumes, vehicle type, and vehicle manufacturer.

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)0.002
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.241
Teacher spread0.233 · 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 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
Published2008
Admission routes1
Has abstractyes

Explore more

Same venueSAE International journal of passenger cars. Electronic and electrical systemsSame topicReal-time simulation and control systemsFrench-language works237,207