MétaCan
Menu
Back to cohort
Record W2021813846 · doi:10.1081/drt-120038571

Drying Foodstuffs with Superheated Steam

2004· article· en· W2021813846 on OpenAlexaff
C. Pronyk, Stefan Cenkowski, W. E. Muir

Bibliographic record

VenueDrying Technology · 2004
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood Drying and Modeling
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsSuperheated steamPulp (tooth)Pulp and paper industrySugar beetStarchSugarMaterials scienceBoiler (water heating)ChemistryComposite materialFood scienceHorticultureWaste managementMedicineEngineering

Abstract

fetched live from OpenAlex

A thin-layer superheated steam drier was constructed with the objective of determining the drying characteristics, drying rates, and the effect of superheated steam on product quality in thin-layers. Results from superheated steam drying experiments with sugar-beet pulp, potatoes, Asian noodles, and spent grains indicate that drying times and rates increase with increasing steam temperature. For sugar-beet pulp it was also found that these changes were more significant than increases seen by hot-air drying under the same conditions and that drying rates were not affected by velocity for hot air but were increased for superheated steam. When quality aspects were examined, superheated steam dried Asian noodles saw both beneficial changes to recovery, adhesiveness, and gumminess while parameters of maximum cutting stress, resistance to compression, and surface firmness saw deleterious effects. Spent grains saw high levels of starch gelatinization and retention of fibre content.

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 score0.001
Threshold uncertainty score0.003

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 head0.010
GPT teacher head0.182
Teacher spread0.172 · 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

Citations119
Published2004
Admission routes1
Has abstractyes

Explore more

Same venueDrying TechnologySame topicFood Drying and ModelingFrench-language works237,207