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SOLID FOODS FREEZE‐DRYING SIMULATION AND EXPERIMENTAL DATA

2005· article· en· W1977670294 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

VenueJournal of Food Process Engineering · 2005
Typearticle
Languageen
FieldEngineering
TopicFreezing and Crystallization Processes
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsSublimation (psychology)Mass transferThermodynamicsNonlinear systemDehydrationMechanicsHeat transferPosition (finance)ChemistryExperimental dataPartial differential equationThermal conductivityMaterials scienceMathematicsPhysicsMathematical analysis

Abstract

fetched live from OpenAlex

ABSTRACT This article presents a mathematical model describing the unsteady heat and mass transfer during the freeze drying of biological materials. The model was built from the mass and energy balances in the dried and frozen regions of the material undergoing freeze drying. A set of coupled nonlinear partial differential equations permitted the description of the temperature and pressure profiles, together with the position of the sublimation interface. These equations were transformed to a finite element scheme and numerically solved using the Newton‐Raphson approach to represent the nonlinear problem and the interface position. Most parameters involved in the model (i.e., thermal conductivity, specific heat, density, heat and mass transfer coefficients etc.) were obtained from experimental data cited in the literature. The dehydration kinetics and the temperature profiles of potato and apple slabs were experimentally determined during freeze drying. The simulation results agreed closely with the water content experimental data. The prediction of temperature profiles within the solid was, however, less accurate.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.150
Threshold uncertainty score0.599

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.027
GPT teacher head0.300
Teacher spread0.274 · 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