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Record W2928748769 · doi:10.18280/mmep.060112

Solar drying of drying agricultural product (Apricot)

2019· article· en· W2928748769 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueMathematical Modelling and Engineering Problems · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood Drying and Modeling
Canadian institutionsnot available
Fundersnot available
KeywordsAgricultureProduct (mathematics)Agricultural engineeringSolar dryerPrunus armeniacaEnvironmental sciencePulp and paper industryAgricultural economicsHorticultureSolar energyGeographyMathematicsEngineeringEconomicsBiology

Abstract

fetched live from OpenAlex

This work allowed us to study the effects of some parameters on drying and specify the most influential. Drying is basically a phenomenon of removal of liquid by evaporation from an apricot. Solar drying is one of the processes that have found application in Algeria, because of the important quantities of solar irradiations that can be exploited in this country.Drying basically comprises of two fundamental and simultaneous processes: heat is transferred to evaporate liquid, and mass is transferred as a liquid or vapor within and the apricot as a vapor from the surface. The experimental study is investigated in the Biskra city of Algeria for drying Apricot in the drying room which it integrated with a solar air collector (new design) which the drying chamber receives the temperature from the solar collector which absorbed from sun. We have to spread the produce on a suitable surface and let it dry in the drying room and added the study of thermal performance of solar air collector and trying with three different airflow rates, namely, 0.018, 0.028 and 0.034 kg/s are conducted. Finally the results have been illustrated with mass water content, product temperature, drying room temperature, outlet temperature in both solar collector and drying room and enthalpy of solar collector and drying room which effect with increase the mass flow rate.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.372
Threshold uncertainty score0.331

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 head0.020
GPT teacher head0.176
Teacher spread0.156 · 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