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Record W2075835179 · doi:10.1002/jsl.3000180206

Delivering synthetic performance with VHVI speciality base fluids

2001· article· en· W2075835179 on OpenAlex
H. Ernest Henderson, B. Swinney, William M. Steckle

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 Synthetic Lubrication · 2001
Typearticle
Languageen
FieldEngineering
TopicLubricants and Their Additives
Canadian institutionsPetro-Canada
Fundersnot available
KeywordsViscosity indexBase (topology)Process engineeringViscosityBiochemical engineeringAutomotive industryComputer scienceBase oilSynthetic oilWork (physics)Mechanical engineeringMaterials scienceEngineeringMathematicsComposite material

Abstract

fetched live from OpenAlex

Abstract Historically, the best lubrication performance has been achieved by the use of synthetic fluids, such as polyalphaolefins or esters. Advanced commercial processing techniques for producing base oils through significant molecular change are now available. In many applications, automotive and industrial products formulated with these high‐quality API Group III speciality base fluids can achieve the same good performance as that from traditional synthetic fluids. This paper represents continued work to understand and demonstrate features of very high viscosity index (VHVI) speciality base fluids. Ultimately, the performance of a finished fluid is the key market requirement. Actual field performance can vary dramatically, even among polyalphaolefin‐based formulations. Several examples are given to show that equivalent high‐level synthetic performance can be delivered through a synergistic balance of VHVI speciality base fluids and additive chemistry.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.830
Threshold uncertainty score0.475

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 head0.009
GPT teacher head0.191
Teacher spread0.182 · 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