Melt strength of linear low‐density polyethylene/low‐density polyethylene blends
Bibliographic record
Abstract
Abstract Linear low‐density polyethylenes and low‐density polyethylenes of various compositions were melt‐blended with a batch mixer. The blends were characterized by their melt strengths and other rheological properties. A simple method for measuring melt strength is presented. The melt strength of a blend may vary according to the additive rule or deviate from the additive rule by showing a synergistic or antagonistic effect. This article reports our investigation of the parameters controlling variations of the melt strength of a blend. The reciprocal of the melt strength of a blend correlates well with the reciprocal of the zero‐shear viscosity and the reciprocal of the relaxation time of the melt. An empirical equation relating the maximum increment (or decrement) of the melt strength to the melt indices of the blend components is proposed. © 2002 Wiley Periodicals, Inc. J Appl Polym Sci 85: 1408–1418, 2002
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".