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Record W2107439412

Development and Trends in Drying of Herbs and Specialty Crops in Western Canada

2005· article· en· W2107439412 on OpenAlexaboutno aff
M. Shaw, Venkatesh Meda, P. Leduc, And L. Tabil

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood Drying and Modeling
Canadian institutionsnot available
Fundersnot available
KeywordsSpeciality chemicalsSpecialtyProduct (mathematics)SimplicityAgricultural engineeringEnvironmental scienceQuality (philosophy)BusinessAgricultural scienceOperations managementWaste managementProcess engineeringPulp and paper industryEngineeringMathematicsEnvironmental engineeringMedicine
DOInot available

Abstract

fetched live from OpenAlex

Heat sensitive properties (aromatic, medicinal, culinary, colour) provide specialty crops with their high market value. Care must be taken when drying specialty crops not to cause extreme losses of heat sensitive properties. Therefore, they must be dried at low temperatures for longer periods of time resulting in large power requirements for dryer operation. In an attempt to establish a basis for a dryer design for preserving western Canadian grown herbs and specialty crops, numerous literature and contacts were consulted in order to compare current dehydration methods based on pre-established criteria. Such criteria involved final product quality, capital cost, power requirements, simplicity, operating cost, capacity, safety and environmental issues. Coriander was dehydrated in two different drying units (thin layer convection and microwave) in order to compare the two methods in terms of final product quality and overall dryer simplicity and operation. A third type of dryer (re-circulating heat pump) was also investigated in numerous literature sources, along with the other two. The conclusions from this study were used to select a dryer on which to base the design of a laboratory scale dehydration unit for herbs and specialty crops.

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 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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.822
Threshold uncertainty score0.638

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 head0.025
GPT teacher head0.217
Teacher spread0.191 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations5
Published2005
Admission routes1
Has abstractyes

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