MétaCan
Menu
Back to cohort
Record W2582114508 · doi:10.11301/jsfe.17.123

Electrohydrodynamic (EHD) Drying of Grape Pomace

2016· article· en· W2582114508 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

VenueJapan Journal of Food Engineering · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicrobial Inactivation Methods
Canadian institutionsDalhousie University
Fundersnot available
KeywordsPomaceElectrohydrodynamicsMoistureMaterials scienceWater contentSpray dryingPulp and paper industryFood scienceEnvironmental scienceChemistryChromatographyComposite material

Abstract

fetched live from OpenAlex

The objective of this research was to find the industrially-accepted processing method of the currently underutilized wet grape pomace prior to subsequent extraction of natural ingredients. Due to high moisture content (2.5-3.0 kg/kg db), thermal drying of pomace is an expensive and time-consuming operation. Therefore, the energy efficiency of non-thermal electrohydrodynamic (EHD) technology as applied for grape pomace drying was extensively studied. The experiments on EHD drying at temperature 20℃ revealed excellent quality of the dry product. Superior energy efficiency of the EHD drying ranging from 600 to 1580 kJ per kg of evaporated water as reported in topical literature was confirmed in our experimental study. These preliminary experiments on the lab-scale showed benefits of EHD drying of heat-sensitive grape pomace to be further transformed into food additives, skin powder and grape oil.

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 score0.080
Threshold uncertainty score0.283

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 head0.006
GPT teacher head0.223
Teacher spread0.217 · 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