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Record W2624450899 · doi:10.13031/aea.31.11195

Evaluation of a Horizontal Air Flow In-Bin Grain Drying System

2015· article· en· W2624450899 on OpenAlexfundno aff

Bibliographic record

VenueApplied Engineering in Agriculture · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood Drying and Modeling
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBinAirflowWater contentMoistureGrain sizeRADIUSGrain dryingEnvironmental scienceFlow (mathematics)Materials scienceMechanicsGeotechnical engineeringComposite materialGeologyEngineeringMechanical engineeringPhysics

Abstract

fetched live from OpenAlex

Abstract. Drying is the major post-harvest operation carried out to reduce the moisture content of harvested grains to increase storage period and reduce storage losses. A prototype horizontal airflow in-bin drying system was developed and evaluated for drying wheat. The results proved that the moisture front moved radially from the center to the sidewall of the bin throughout the grain bulk and there were no significant differences (p<0.05) in drying at the bottom, middle, and top layers of the grain bulk when the airflow control valve was set at 2 m (equal to radius of the bin) from the top surface of the grain bulk. When the airflow control valve was set at 4 m (diameter of the bin), there were significant differences among the drying patterns at the three different heights. The results of the air pressure measurements showed that the air pressure distribution was equal around the radius of the bin. These results indicated that the current prototype dried the grain evenly from bottom to top of the grain bulk and can be used effectively at commercial level to dry grain in hopper bottom bins (tested) as well as in flat-bottom bins.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

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 head0.024
GPT teacher head0.201
Teacher spread0.177 · 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 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

Citations4
Published2015
Admission routes1
Has abstractyes

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