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Record W1967194864 · doi:10.5539/mas.v3n4p30

Two-Stage Engine Mapping for the Calibration of Carbon Monoxide Emission

2009· article· en· W1967194864 on OpenAlexvenueno aff
Recep Kozan, Mersin Gokce

Bibliographic record

VenueModern Applied Science · 2009
Typearticle
Languageen
FieldChemical Engineering
TopicAdvanced Combustion Engine Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsCalibrationAutomotive engineeringStage (stratigraphy)Diesel engineCarbon monoxideTable (database)Diesel fuelComputer scienceTorqueEnvironmental sciencePhysicsMathematicsEngineeringStatisticsChemistryThermodynamicsData miningGeology

Abstract

fetched live from OpenAlex

The calibration of a model’s parameters due to desired objectives is the main definition of model-based calibration. The calibration of a diesel engine’s two-stage model to get minimum carbon monoxide (CO) emission is described in this paper.The data used in this study is collected in an engine test bench which can measure CO emission of the engine for different cam angle, engine speed and torque values. The different two-stage models are created by different local and global model functions. The best model among these models is chosen by statistical parameters.Afterwards, this model is used for the calibration. The optimization of cam angle to minimize CO emissions is described. At the end of the calibration, the look-up table can be filled up with optimum cam angle values to be used in an electronic control unit of a diesel engine (ECU).

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 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.926
Threshold uncertainty score0.338

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.020
GPT teacher head0.256
Teacher spread0.236 · 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 teacher head, 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

Citations7
Published2009
Admission routes1
Has abstractyes

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