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Record W2052871268 · doi:10.1002/app.30710

A relatively simple method for calculating Mark‐Houwink parameters using basic definitions

2009· article· en· W2052871268 on OpenAlexaff
Jennifer L. Braun, John F. Kadla

Bibliographic record

VenueJournal of Applied Polymer Science · 2009
Typearticle
Languageen
FieldEngineering
TopicMaterial Properties and Processing
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsDispersityPolystyreneIntrinsic viscosityPolymer scienceMaterials scienceSize-exclusion chromatographyPolymer chemistryCellulose acetateCelluloseChemistryPolymerOrganic chemistryComposite material

Abstract

fetched live from OpenAlex

Abstract A method for calculating Mark‐Houwink parameters from intrinsic viscosity, size exclusion, and number‐ or weight‐average molecular weight data using basic definitions was presented. Polydispersity is not an issue and no calibration curve is required as long as all of the aforementioned data are available. Results for polystyrene, cellulose acetate, and polymethylhydrosiloxane are discussed. © 2009 Wiley Periodicals, Inc. J Appl Polym Sci, 2009

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)0.003
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.016
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.011
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.002
Science and technology studies0.0010.001
Scholarly communication0.0020.004
Open science0.0030.002
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0160.018

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 head0.052
GPT teacher head0.288
Teacher spread0.236 · 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 designTheoretical or conceptual
Domainnot available
GenreMethods

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

Citations6
Published2009
Admission routes1
Has abstractyes

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