MétaCan
Menu
Back to cohort

The Determination of Semi-Solid Processing Ability Using a Novel Rheo-Characterizer Apparatus

2008· article· en· W2170061331 on OpenAlexafffund
Manel da Silva, Alain Lemieux, Hugues Blanchette, X.-G. Chen

Bibliographic record

VenueDiffusion and defect data, solid state data. Part B, Solid state phenomena/Solid state phenomena · 2008
Typearticle
Languageen
FieldEngineering
TopicMetallurgy and Material Forming
Canadian institutionsUniversité du Québec à Chicoutimi
FundersNatural Sciences and Engineering Research Council of CanadaUniversité du Québec à Chicoutimi
KeywordsSlurryMaterials scienceMicrostructureGrain sizeComposite material

Abstract

fetched live from OpenAlex

The “Rheo-Characterizer” is an apparatus designed to assess the quality control of the semi-solid material. The working principle is based on the recording and subsequent analysis of the force required to transversally cut the semi-solid slurry at constant speed. Semi-solid slurries produced under different conditions while using the SEED process were analyzed with the “Rheo- Characterizer”. The TiB2 grain refiner was also added to evaluate the impact on the microstructure and the cutting force. The microstructural features were measured and the grain size was analyzed for the different processing conditions. The effect of the cutting temperature on the resulting curve was also investigated. The results show that the “Rheo-Characterizer” is capable of differentiating between the varied microstructural morphologies and the solid fraction of the billet.

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.

How this classification was reachedexpand

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.840
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0010.003
Open science0.0020.003
Research integrity0.0000.001
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 head0.053
GPT teacher head0.295
Teacher spread0.242 · 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

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2008
Admission routes2
Has abstractyes

Explore more

Same venueDiffusion and defect data, solid state data. Part B, Solid state phenomena/Solid state phenomenaSame topicMetallurgy and Material FormingFrench-language works237,207