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Record W2088677026 · doi:10.4271/2014-32-0087

Gaseous and Particulate Emissions Using Isobutanol-Extended Fuel in Recreational Marine Two-Stroke and Four-Stroke Engines

2014· article· en· W2088677026 on OpenAlexaff
Jeff R. Wasil, Thomas Wallner

Bibliographic record

VenueSAE international journal of fuels and lubricants · 2014
Typearticle
Languageen
FieldEngineering
TopicVehicle emissions and performance
Canadian institutionsBombardier (Canada)Bombardier Recreational Products (Canada)
FundersOffice of Energy Efficiency and Renewable EnergyMcKnight FoundationU.S. Department of Energy
KeywordsIsobutanolTwo-stroke engineParticulatesFour-stroke engineStroke (engine)Environmental scienceAutomotive engineeringChemistryEngineeringInternal combustion engineAerospace engineeringOrganic chemistryCombustionAlcohol

Abstract

fetched live from OpenAlex

<div class="section abstract"><div class="htmlview paragraph">Biologically derived isobutanol, a four carbon alcohol, has an energy density closer to that of gasoline and has potential to increase biofuel quantities beyond the current ethanol blend wall. When blended at 16 vol% (iB16), it has identical energy and oxygen content of 10 vol% ethanol (E10).</div><div class="htmlview paragraph">Engine dynamometer emissions tests were conducted on two open-loop electronic fuel-injected marine outboard engines of both two-stroke and four-stroke designs using indolene certification fuel (non-oxygenated), iB16 and E10 fuels. Total particulate emissions were quantified using Sohxlet extraction to determine the amount of elemental and organic carbon. Data indicates a reduction in overall total particulate matter relative to indolene certification fuel with similar trends between iB16 and E10. Gaseous and PM emissions suggest that iB16, relative to E10, could be promising for increasing the use of renewable fuels in recreational marine engines and fuel systems.</div></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.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.440
Threshold uncertainty score0.424

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 head0.014
GPT teacher head0.252
Teacher spread0.238 · 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 designObservational
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

Citations4
Published2014
Admission routes1
Has abstractyes

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