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Analyzing Failures in Pistons of Racing Car Engines

2019· book-chapter· en· W3121842538 on OpenAlex

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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueASM International eBooks · 2019
Typebook-chapter
Languageen
FieldEngineering
TopicMechanical Failure Analysis and Simulation
Canadian institutionsnot available
Fundersnot available
KeywordsPiston (optics)AbrasiveMaterials scienceMetallurgySofteningAlloyComposite materialForensic engineeringEngineering

Abstract

fetched live from OpenAlex

Abstract To determine the effect of severe service on cast 357 aluminum pistons, a metallurgical evaluation was made of four pistons removed from the engine of the Hawk-Offenhauser car which had been driven by Rich Muther in the first Ontario, California 500 race. The pistons were studied by visual inspection, hardness traverses, radiography, dye penetrant inspection, chemical analysis, macrometallography, optical microscopy, and electron microscopy. The crown of one piston had a rough, crumbly deposit, which was detachable with a knife. Two pistons had remains of carbonaceous deposits. The fourth was severely hammered. It was concluded that the high temperatures developed in this engine created an environment too severe for 357 aluminum. Surfaces were so hot that the low-melting constituent melted. Then, the alloy oxidized rapidly to form Al2O3, an abrasive which further aggravated problems. The temperature in much of the piston was high enough to cause softening by overaging, lowering strength.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.876
Threshold uncertainty score0.904

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 head0.221
Teacher spread0.211 · 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