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Record W2100209967 · doi:10.1177/8756087904045438

Advanced Solution Process Technology for Cast Film Applications: New Opportunities Using Novel Catalyst Technologies

2004· article· en· W2100209967 on OpenAlexafffund
Norman Aubee, K. W. Ho, Philippa J. Hocking, Tony Tikuisis

Bibliographic record

VenueJournal of Plastic Film & Sheeting · 2004
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Science and PVC
Canadian institutionsNova Chemicals (Canada)
FundersUniversity of WaterlooNOVA Chemicals
KeywordsMaterials scienceRheologyPolyethyleneLinear low-density polyethylenePolymerizationCatalysisComposite materialPolymerProcess engineeringPolymer scienceOrganic chemistry

Abstract

fetched live from OpenAlex

Using a new advanced solution technology, NOVA Chemicals Corporation has developed an improved family of polyethylene resins, some of which are ideally suited for specific applications, for example, medium density for hygiene films and linear low density for stretch films. This new technology has been used to produce polyethylene resins with tailored molecular structure and processability. This new technology is based on an improved solution polymerization process using a new Ziegler-Natta catalyst. The rheological properties, particularly draw resonance, together with the physical and mechanical properties of cast films made from these new resins are presented in this paper. In addition, there is a discussion on the results of an investigation on the effects that different types and levels of stabilizers have on resin processing behavior and film stability.

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)0.001
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: none
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.002

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 head0.299
Teacher spread0.249 · 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

Citations2
Published2004
Admission routes2
Has abstractyes

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