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Record W1982023054 · doi:10.5539/jas.v2n2p147

Screw Conveyors Power and Throughput Analysis during Horizontal Handling of Paddy Grains

2010· article· en· W1982023054 on OpenAlexvenueno aff
Hemad Zareiforoush, M. H. Komarizadeh, Mohammad Reza Alizadeh, Mahdi Masoomi

Bibliographic record

VenueJournal of Agricultural Science · 2010
Typearticle
Languageen
FieldEngineering
TopicAgricultural Engineering and Mechanization
Canadian institutionsnot available
FundersUrmia University
KeywordsRotational speedThroughputPower (physics)Point (geometry)Power pointMaterials scienceMechanical engineeringAutomotive engineeringComputer scienceEngineeringMathematicsPhysicsGeometry

Abstract

fetched live from OpenAlex

The tests reported in this research were conducted to determine the effects of those parameters believed to have thegreatest influence upon horizontal screw conveyors throughput capacity and power requirement when handlingpaddy grains. Two screw conveyors with diameters of 20 and 25 cm were evaluated at four screw clearances (6, 9,12 and 15 mm) and five screw rotational speeds (200, 300, 400,500 and 600 rpm). The results revealed that forboth the conveyors tested, with increasing the screw rotational speed, the conveyor capacity increased andreached to a maximum point and after the point, the volumetric capacity decreased. Increasing the screw speedcaused the volumetric efficiency and power requirements of the conveyors to be decreased and increased,respectively. The throughput capacity and power requirement of the conveyors increased (P

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.002
GPT teacher head0.176
Teacher spread0.174 · 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 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

Citations22
Published2010
Admission routes1
Has abstractyes

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