MétaCan
Menu
Back to cohort
Record W1965139194 · doi:10.1504/ijpti.2012.050982

Effect of microwave and hot air drying on flax straw at controlled temperatures

2012· article· en· W1965139194 on OpenAlexaff
Gopu Raveendran Nair, Pansa Liplap, Yvan Gariépy, G. S. V. Raghavan

Bibliographic record

VenueInternational Journal of Postharvest Technology and Innovation · 2012
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood Drying and Modeling
Canadian institutionsMcGill University
Fundersnot available
KeywordsMicrowaveMaterials scienceUltimate tensile strengthComposite materialWater contentMoistureAir dryerStrawAir temperatureMicrowave ovenPulp and paper industryChemistryWaste managementMeteorology

Abstract

fetched live from OpenAlex

Flax stems were subjected to microwave drying at controlled temperatures. The rate of drying was then compared with conventional hot air drying. The product temperature was maintained at 40°C, 60°C and 80°C for both microwave and hot air drying. The moisture content of flax stem was about 70% (wet basis). The microwave drying was conducted in a microwave apparatus which recorded mass, product temperature, incident microwave power, reflected microwave power and inlet/outlet air temperature. The final moisture content for experiment was set to 9% (wet basis). Microwave-convective drying ensured about 30% to 70% reduction of drying time for drying flax straw as compared to hot air drying. Curve fitting with different mathematical models were carried out and all the models were fitted well for both hot air and microwave drying. The tensile strength of flax straw, measured with an Instron apparatus, increased with an increase in the processing temperature of both processes. Hot air dried flax straw showed the greatest tensile strength and modulus of elasticity at processing temperatures of 60°C and 80°C with a significant difference.

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)0.001
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 score0.051
Threshold uncertainty score0.141

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.011
GPT teacher head0.256
Teacher spread0.246 · 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 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

Citations13
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Postharvest Technology and InnovationSame topicFood Drying and ModelingFrench-language works237,207