MétaCan
Menu
Back to cohort
Record W3013719721 · doi:10.1115/1.2011-jan-2

Engineering Taste

2011· article· en· W3013719721 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

VenueMechanical Engineering · 2011
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood Chemistry and Fat Analysis
Canadian institutionsWeyerhauser (Canada)
Fundersnot available
KeywordsDifferential scanning calorimetryNucleationThermodynamicsWork (physics)Materials scienceSupercoolingMechanical engineeringPhysicsEngineering

Abstract

fetched live from OpenAlex

This article discusses a study focusing on developing a mathematical model for creating and modifying the structure of chocolates. In the experimental study at the University of Birmingham's Centre for Formulation Engineering, researchers cooled and heated chocolate through rapid programmed temperature changes and then studied what happened using differential scanning calorimetry. The data was fitted to six kinetic processes. To make the modeling easier, the system of six polymorphs and liquid chocolate was simplified to model only three materials: stable solids, unstable solids, and melt. Then equations were developed to describe the nucleation of crystals, growth of stable and unstable phases, and the melting of the stable and unstable solids. The model developed simplifies the number of crystal forms, but this simplification makes it possible to model differential scanning calorimetry data. Once fitted to differential scanning calorimetry data over a range of cooling rates, the model can then be used both to predict behavior and to explain what is happening in the process. The model can be used to show how ‘frozen cone’ methods work.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.082
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.150
Teacher spread0.134 · 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