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Record W2106280343

Dehydration dynamics of potatoes in superheated steam and hot air

2000· article· en· W2106280343 on OpenAlexvenueno aff
Zhaohui Tang, Stefan Cenkowski

Bibliographic record

VenueCanadian agricultural engineering · 2000
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood Drying and Modeling
Canadian institutionsnot available
Fundersnot available
KeywordsSuperheated steamDehydrationMoistureChemistryThermal diffusivitySuperheatingWater contentAtmospheric pressureCondensationDew pointWater vaporThermodynamicsMeteorologyOrganic chemistry
DOInot available

Abstract

fetched live from OpenAlex

Tang, Z. and Cenkowski, S. 2000. Dehydration dynamics of potatoes in superheated steam and hot air. Can. Agric. Eng. 42:043-049. Superheated-steam at atmospheric pressure is an alternative drying medium for dehydrating materials insensitive to temperature equal to or above 100°C. This research compared the dehydration characteristics, temperature histories, drying rates, and overall moisture diffusivities of cylindrical potato samples exposed to superheated steam and hot air at 125, 145, and 165°C. A small amount of moisture (0.18 to 0.47 kg/kg db, dry basis) dependent on the steam temperature was gained from steam condensation on the sample surface during the warm-up period from the superheated-steam. The temperature of the drying medium had a greater effect on the drying rate, overall moisture diffusivity, and consequently dehydration time for the superheatedsteam dehydration than for the hot-air dehydration. Increasing the temperature from 125 to 165°C decreased the dehydration time by 60 and 24% for the superheated-steam and hot-air dehydration, respectively. A constant-rate drying period was only observed with superheated steam at 125 and 145°C. There existed an inversion temperature point between 145 to 165°C for the first dehydration stage

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)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.999
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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 head0.005
GPT teacher head0.144
Teacher spread0.139 · 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

Citations83
Published2000
Admission routes1
Has abstractyes

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