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Record W587635663 · doi:10.1520/stp14475s

Cold Starting and Pumpability Studies in Modern Engines — Results from the ASTM D02.07C Low Temperature Engine Performance Task Force Activities: Test Oil Selection and Rheological Analysis

2000· book-chapter· en· W587635663 on OpenAlexaff
FW Girshick, EF De Paz, CJ May, KO Henderson, RB Rhodes, Spyros I. Tseregounis, LH Ying

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldEngineering
TopicHydraulic and Pneumatic Systems
Canadian institutionsImperial Oil (Canada)
Fundersnot available
KeywordsSelection (genetic algorithm)RheologyTest (biology)Task (project management)Computer scienceEnvironmental scienceEngineeringMaterials scienceArtificial intelligenceComposite materialBiologyBotanySystems engineering

Abstract

fetched live from OpenAlex

Two series of test oils were obtained for the Low Temperature Engine Performance (LTEP) task force activities: (a) SAE 0W-30, 5W-30, 10W-30, 15W-40, 20W-50 and 25W-30 grades were fully on-specification commercial oils (LTEP 1–7), used in cold starting and Phase I pumpability studies, (b) experimental test oils for Phase II pumpability work (LTEP 20 series) either did not meet the then-current SAE J300 pumping viscosity (TP-1 MRV) limits, or showed significant D5133 gelation index values, and included nominal SAE 5W-30, 10W-30 and 15W-40 grades. LTEP participating labs provided rheological data on the oils including measurements by D 445, D 3829, D 4684, D 5133, and D 5293. Multi-temperature MRV and CCS measurements allowed correlation to the exact engine test temperatures by fitting viscosity and yield stress data to equations. The functional form of the equations is presented and explained, and the fitted coefficients are tabulated. Gelation index and gelation index temperature values, were simply averaged for each oil. For the D 3829 and D 4684 methods that measure yield stress in addition to viscosity, an adjustment was made to the measured viscosity before fitting, to allow for yield stress effects.

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 categoriesMeta-epidemiology (narrow)
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 score0.112
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.014
GPT teacher head0.209
Teacher spread0.195 · 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.

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
Published2000
Admission routes1
Has abstractyes

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