MétaCan
Menu
Back to cohort
Record W1988081498 · doi:10.1063/1.1576502

Structural basis for the yield stress in plastic disperse systems

2003· article· en· W1988081498 on OpenAlexaff
Alejandro G. Marangoni, Michael A. Rogers

Bibliographic record

VenueApplied Physics Letters · 2003
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood Chemistry and Fat Analysis
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsYield (engineering)Materials scienceFractal dimensionYield surfaceRheologyStress (linguistics)FractalWork (physics)Particle (ecology)Composite materialThermodynamicsMathematicsPhysicsFinite element methodMathematical analysisConstitutive equation

Abstract

fetched live from OpenAlex

The apparent yield stress is a fundamental rheological characteristic of a plastic material. In this work, we propose a model that relates the structure of a fractal particle network to its yield stress. The model predicts the yield stress to be a function of particle size, solid-liquid surface free energy, the amount of network material, and the fractal dimension of the network. The predicted yield stress corresponded to the stress at the limit of linearity in fat crystal networks. Moreover, a structural definition for the yield stress is proposed.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.017
GPT teacher head0.192
Teacher spread0.175 · 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

Citations95
Published2003
Admission routes1
Has abstractyes

Explore more

Same venueApplied Physics LettersSame topicFood Chemistry and Fat AnalysisFrench-language works237,207