MétaCan
Menu
Back to cohort
Record W1987503450 · doi:10.1115/1.2978996

On Moore’s Law and Its Application to Spark Ignition Engine Technology

2008· article· en· W1987503450 on OpenAlex
Marc LaViolette

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

VenueJournal of Engineering for Gas Turbines and Power · 2008
Typearticle
Languageen
FieldDecision Sciences
TopicInnovation Diffusion and Forecasting
Canadian institutionsRoyal Military College of Canada
Fundersnot available
KeywordsIgnition systemSPARK (programming language)ElectronicsSpark-ignition engineLogistic functionFunction (biology)Power lawExponential functionMoore's lawInternal combustion engineCombustionLawAutomotive engineeringComputer scienceEngineeringElectrical engineeringMathematicsPhysicsThermodynamicsStatisticsPolitical science

Abstract

fetched live from OpenAlex

Moore’s law relates how the integration of semiconductors has progressed in time. This research shows that the exponential trend shown in the electronics manufacturing industry can have applications elsewhere. This study shows that the internal combustion engine followed the same trend for over 70 years. Though not the most used engine variable, engine power density shows the same trends for engines as transistor density does for microchips. This now mature technology has ended its period of rapid growth. However, the present day engine trends can show how Moore’s law can be extended to include the slower growth of long established technologies. Because exponential growth cannot go on forever, the extension Moore’s law requires that the logistic function be used. The new function also allows one to predict a theoretical value for maximum power density.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.688
Threshold uncertainty score0.262

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.033
GPT teacher head0.294
Teacher spread0.261 · 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