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Record W2885673436 · doi:10.14510/araj.2017.4123

Compaction of Metal-Ceramic Powder Mixture. Part.1

2017· article· en· W2885673436 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.

venuePublished in a venue whose home country is Canada.
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

VenueJournal of the American Romanian Academy of Arts and Sciences · 2017
Typearticle
Languageen
FieldEngineering
TopicPowder Metallurgy Techniques and Materials
Canadian institutionsnot available
Fundersnot available
KeywordsCompactionCeramicMaterials scienceMetal powderMetalMetallurgyComposite material

Abstract

fetched live from OpenAlex

The strength of green compact is influenced by the characteristics of the powders (apparent or tap density, particle size and shape, internal pores etc.), the processing parameters (applied force, pressing type, temperature) and testing conditions (strain rate etc.). For elucidation the phenomena that arise in the compaction of metallic and ceramic powders mixtures, in the first part of the paper, we present the mechanisms that influence the densification behaviour, the post-elastic effect, the compressibility of different (e.g. Al or Cu alloys -SiC/Al 2 O 3 / TiO2) mixture powders and the particularities of each of them during the three stages of compaction.

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)0.001
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 score0.480
Threshold uncertainty score0.309

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.034
GPT teacher head0.295
Teacher spread0.261 · 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