MétaCan
Menu
Back to cohort
Record W1589221021 · doi:10.4271/2004-01-2616

The Predictive Cruise Control – A System to Reduce Fuel Consumption of Heavy Duty Trucks

2004· article· en· W1589221021 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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueSAE technical papers on CD-ROM/SAE technical paper series · 2004
Typearticle
Languageen
FieldEngineering
TopicVehicle emissions and performance
Canadian institutionsnot available
Fundersnot available
KeywordsTruckHeavy dutyCruise controlAutomotive engineeringFuel efficiencyCruiseModel predictive controlConsumption (sociology)Computer scienceControl (management)Environmental scienceEngineeringAerospace engineering

Abstract

fetched live from OpenAlex

<div class="htmlview paragraph">Predictive Cruise Control (PCC) is a system that enhances and works in combination with the existing Conventional Cruise Control. Based on elevation information captured in a 3D map and a predictive algorithm, PCC allows the vehicle speed to vary around the cruise control set speed within a defined speed band in an effort to reduce fuel consumption. As fuel consumption is a major portion of a truck's life cycle costs (LCC) and cruise control is used extensively in the United States and Canada, PCC can significantly reduce the truck's LCC.</div>

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)0.001
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.980
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.009
GPT teacher head0.233
Teacher spread0.224 · 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