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Record W2069965652 · doi:10.1080/07373930601031463

Thin-Layer Drying of Flax Fiber: II. Modeling Drying Process Using Semi-Theoretical and Empirical Models

2006· article· en· W2069965652 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

VenueDrying Technology · 2006
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood Drying and Modeling
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsAir temperatureThin layerMaterials scienceFiberWood dryingHumidityRelative humidityWater contentEmpirical modellingThermodynamicsComposite materialChemistryLayer (electronics)MoistureMeteorologyPhysics

Abstract

fetched live from OpenAlex

Thin-layer drying experiments were performed for drying flax fiber under four different drying conditions. In all drying treatments the absolute humidity of drying air was 0.0065 kg of water per kg of dry air, but the drying temperature were 30, 50, 70, and 100°C. The drying process was modeled using the drying data and five semi−theoretical and empirical models cited in different literatures. From the five tested models, the Page model gave the best fitting for experimental data with R 2 equal to 0.99, for all treatments. The estimated drying constants at different drying temperatures were highly correlated with drying air temperature. The drying constants were also highly correlated with the calculated coefficient of diffusions.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.642
Threshold uncertainty score0.723

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.040
GPT teacher head0.276
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