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Record W1997769293 · doi:10.1243/095765005x7538

Application of moisture transfer models to solids drying

2005· article· en· W1997769293 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueProceedings of the Institution of Mechanical Engineers Part A Journal of Power and Energy · 2005
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood Drying and Modeling
Canadian institutionsOntario Tech University
FundersNatural Sciences and Engineering Research Council of CanadaFirat Üniversitesi
KeywordsWater contentThermal diffusivityMoistureMaterials scienceWood dryingSlabAirflowMass transferThermodynamicsEnvironmental scienceComposite materialGeotechnical engineeringGeologyPhysics

Abstract

fetched live from OpenAlex

In this paper, a comprehensive experimental investigation is conducted to measure the moisture content distributions within slab cut potato pieces during drying at temperatures of 60, 70, and 80°C and flow velocities of 1.0 and 1.5 m/s. Four drying models are employed to determine the drying process parameters (drying coefficient, lag factor, and half-drying time) and moisture transfer parameters (moisture diffusivity and moisture transfer coefficient), and to calculate moisture content distributions and compare them with extensive sets of experimental moisture data measured during the drying of slab cut potato slices at different drying air temperatures and flow velocities. Good agreement is obtained between the calculations and experimental measurements for the cases. In addition, experimental drying times are determined and compared with those obtained by the four different drying models. The results show that all four models are well able to determine the drying parameters and moisture content distributions. The experimental data and model findings are expected to be useful to the drying industry.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.094
Threshold uncertainty score0.134

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)0.0000.000

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 head0.014
GPT teacher head0.199
Teacher spread0.186 · 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