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Polymer Processing: Extrusion

2017· other· en· W2903690399 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

VenueEncyclopedia of Polymer Science and Technology · 2017
Typeother
Languageen
FieldChemical Engineering
TopicRheology and Fluid Dynamics Studies
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsExtrusionPlastics extrusionMixing (physics)Materials scienceMolding (decorative)Die (integrated circuit)Blow moldingExtrusion mouldingComposite materialMechanical engineeringRaw materialInjection molding machineMelting pointEngineeringMoldNanotechnologyChemistry

Abstract

fetched live from OpenAlex

Abstract Extruders are the most common machines in the plastics processing industry. Extruders are used not only in extrusion operations but also in molding operations, for example, injection molding and blow molding. Essentially every plastic part has gone through an extruder at one point or another; in many cases, more than once. In typical plastics extrusion processing, a viscous melt under pressure is forced through a shaping die in a continuous stream. The feedstock may enter the extrusion device in the molten state, but more commonly it consists of solid particles that must be subjected in the extruder to melting, mixing, and pressurization. This article discusses extruders and the extrusion process in detail, including screw design, dies, mixing, melting, conveying, and degassing. It also includes a comparison between different configurations and reports on their advantages and limitations.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.606
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.004
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.000
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 head0.005
GPT teacher head0.236
Teacher spread0.230 · 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