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Record W2321482987 · doi:10.1515/secm.2008.15.2.87

Processing and Properties of a Ni-ZrO2 P/M Composite

2008· article· en· W2321482987 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.

Bibliographic record

VenueScience and Engineering of Composite Materials · 2008
Typearticle
Languageen
FieldEngineering
TopicAdvanced materials and composites
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMaterials scienceComposite numberMaterials processingComposite materialIndustrial chemistryProcess engineeringBiochemical engineeringEngineering

Abstract

fetched live from OpenAlex

The powder metallurgy processing route is used for preparing Ni-matrix MMC by incorporating ultrafine Zr0 2 powder that is agglomerated into micron sized clusters. After blending with ball milling, the heated porous MMC is consolidated using the compression of a cam plastometer. Characterization of the MMC using SEM and EDAX reveals that most of the Zr0 2 clusters existing in the Zr0 2 feed powder can be nearly completely broken up by the ball milling process during blending. However, residual Zr0 2 clusters in a loose structure are preserved in the final sintered Ni-Zr0 2 material. After compression using a Cam plastometer, the clusters are not broken up but are flattened into discs but not densified or penetrated with Ni. A de-agglomeration process was attempted as an alternative blending process in order to achieve a better distribution of Zr0 2 . The results showed a modest improvement in the breakup of the clusters. Despite the presence of these clusters, it is shown that the dispersed Zr0 2 powders do increase activation energy for the flow stress of the MMC.

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 score0.006
Threshold uncertainty score0.548

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 head0.011
GPT teacher head0.190
Teacher spread0.179 · 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