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Impact of Engine Lubricant Properties on Regulated Gaseous Emissions of 2000-2001 Model-Year Gasoline Vehicles

2004· article· en· W2019190101 on OpenAlexaff
Thomas D. Durbin, Claudia Sauer, John T. Pisano, Sam H. Rhee, Tao Huai, John W. Miller, G. I. Mackay, John Robbins, Heather A. Gamble, Albert M. Hochhauser, Michael C. Ingham, Robert A. Gorse, Loren K. Beard

Bibliographic record

VenueJournal of the Air & Waste Management Association · 2004
Typearticle
Languageen
FieldEngineering
TopicVehicle emissions and performance
Canadian institutionsGolder Associates (Canada)
FundersHonda Development and Manufacturing of America
KeywordsGasolineLubricantSulfur dioxideIdleAutomotive engineeringEnvironmental scienceAnalytical Chemistry (journal)EngineeringChemistryWaste managementEnvironmental chemistryMechanical engineeringComputer science

Abstract

fetched live from OpenAlex

The impact of the sulfur (S) content in lubricating oil was evaluated for four ultra-low-emission vehicles and two super-ultra-low-emission vehicles, all with low mileage. The S content in the lube oils ranged from 0.01 to 0.76%, while the S content of the gasoline was fixed at 0.2 ppmw. Vehicles were configured with aged catalysts and tested over the Federal Test Procedure, at idle and at 50-mph cruise conditions. In all testing modes, variations in the S level of the lubricant did not significantly affect the regulated gas-phase tailpipe emissions. In addition to the regulated gas-phase emissions, a key element of the research was measuring the engine-out sulfur dioxide (SO2) in near-real-time. This research used a new methodology based on a differential optical absorption spectrometer (DOAS) to measure SO2 from the lubricants used in this study. With the DOAS, the contribution of SO2 emissions for the highest-S lubricant was found to range from less than 1 to 6 ppm on a gasoline S equivalent basis over the range of vehicles and test cycles used. The development and operation of the DOAS is discussed in this paper.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.134
Threshold uncertainty score0.317

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 head0.010
GPT teacher head0.215
Teacher spread0.205 · 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 designSimulation or modeling
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

Citations1
Published2004
Admission routes1
Has abstractyes

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