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Record W3124427007 · doi:10.31399/asm.hb.v15.a0005351

Molten-Metal Filtration

2008· book-chapter· en· W3124427007 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

VenueASM International eBooks · 2008
Typebook-chapter
Languageen
FieldEngineering
TopicIndustrial Engineering and Technologies
Canadian institutionsPyrogenesis (Canada)
Fundersnot available
KeywordsFiltration (mathematics)Molten metalCartridgeMaterials scienceAluminiumMoldProcess engineeringParticle (ecology)MetallurgyMechanical engineeringComposite materialEngineeringMathematics

Abstract

fetched live from OpenAlex

Abstract In the handling of molten aluminum, it is fairly common to use filters as a part of the melting unit and in the gating and/or riser system. This article describes the methods of in-furnace and in-mold filtration, with emphasis on the filtration of molten aluminum. It discusses the factors that influence the formation of inclusions. The article describes the three basic methods of mechanically removing or separating inclusions from molten metal. The methods include sedimentation, flotation, and positive filtration. The article provides a discussion on the types of molten-metal filters, including bonded-particle filters, cartridge filters, and ceramic foam filters. It lists the factors that are important in achieving optimum performance of any in-furnace filtering application. The article concludes with information on filtered metal quality.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.890
Threshold uncertainty score1.000

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 integrity0.0000.001
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 head0.019
GPT teacher head0.190
Teacher spread0.171 · 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