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INVESTIGATION INTO THE DENSITY OF POLYSTERENE FOAM MODELS WHEN IMPLEMENTING THE RESOURCE-SAVING FABRICATION TECHNOLOGY OF THIN-WALL ALUMINUM SHEET

2015· article· en· W2286820817 on OpenAlexaboutno aff
V. B. Deev, K. V. Ponomareva, A. S. Yudin

Bibliographic record

VenueIzvestiya Non-Ferrous Metallurgy · 2015
Typearticle
Languageen
FieldMaterials Science
TopicMaterial Properties and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsFabricationMaterials scienceAluminiumResource (disambiguation)NanotechnologyComposite materialComputer science

Abstract

fetched live from OpenAlex

The influence of density of polysterene foam models on the quality of thin-wall castings of the cap of the gas analyzer case made of AK7 alloy smelted based on wastes of home manufacture (the charge contained 50–55 % secondary materials) is investigated in conditions of LLC SPE «Vektor Mashinostroeniya» (Novokuznetsk). To foam polysterene (produced by «STYROCHEM», Montreal, Canada) and fabricate the models, a GK-100-3M autoclave was used. Varying temporal-and-time autoclave modes, we obtained different densities of the model ρ = 0,017, 0,019, 0,022, 0,024, and 0,026 g/cm3. Based on the experimental investigations, the values of this index (ρ = 0,022÷0,024 g/cm3), at which the model possesses the required surface quality, stiffness, and burnability promoting the minimization of linear defects (mismatch with required geometric sizes, seal, misrun) when fabricating thin-wall casts with specified properties, are determined and substantiated.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.050
GPT teacher head0.254
Teacher spread0.204 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations0
Published2015
Admission routes1
Has abstractyes

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Same venueIzvestiya Non-Ferrous MetallurgySame topicMaterial Properties and ApplicationsFrench-language works237,207