MétaCan
Menu
Back to cohort
Record W1974804003 · doi:10.1115/1.2744429

Effects of Molybdenum Content and Heat Treatment on Mechanical and Tribological Properties of a Low-Carbon Stellite® Alloy

2006· article· en· W1974804003 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Engineering Materials and Technology · 2006
Typearticle
Languageen
FieldEngineering
TopicMetal and Thin Film Mechanics
Canadian institutionsNational Research Council CanadaCarleton University
FundersNational Research Council CanadaNatural Sciences and Engineering Research Council of Canada
KeywordsStelliteMaterials scienceAlloyMicrostructureTribometerMetallurgyMolybdenumTribologyScanning electron microscopeComposite materialAbrasion (mechanical)

Abstract

fetched live from OpenAlex

The chemical composition of Stellite® 21 alloy was modified by doubling the molybdenum (Mo) content for enhanced corrosion and wear resistance. The specimens were fabricated using a casting technique. Half of the specimens experienced a heat treatment at 1050°C for an hour. The microstructure and phase analyses of the specimens were conducted using electron scanning microscopy and X-ray diffraction. The mechanical properties of the specimens were determined in terms of the ASTM Standard Test Method for Tension Testing of Metallic Materials (E8-96). The mechanical behaviors of individual phases in the specimen materials were investigated using a nano-indentation technique. The wear resistance of the specimens was evaluated on a ball-on-disk tribometer. The experimental results revealed that the increased Mo content had significant effects on the mechanical and tribological properties of the low-carbon Stellite® alloy and the heat treatment also influenced these properties.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.397

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 integrity0.0000.000
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 head0.164
Teacher spread0.154 · 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