MétaCan
Menu
Back to cohort
Record W4391471174 · doi:10.30588/jeemm.v7i2.1513

Analisis Kecepatan Putar Silinder Perontok Terhadap Kinerja Mini Power Thresher Hasil Rekayasa UPJA Desa Sungai Kelambu

2023· article· en· W4391471174 on OpenAlexaff
Suhendra Suhendra, Deliana Pridaningsih, Lang Jagat, Feby Nopriandy

Bibliographic record

VenueJurnal Engine Energi Manufaktur dan Material · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Optimization Techniques
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsMaterials scienceComposite material

Abstract

fetched live from OpenAlex

Various types of power threshers have been developed to suit conditions in the field. The development of the power thresher was also carried out by UPJA Sungai Kelambu Village, Sambas Regency by making a mini power thresher that was designed to suit the conditions of the land and the needs of local farmers. The purpose of this research was to obtain a relationship between threshing rotational speed and performance on the mini power thresher. The threshing cylinder rotational speed was varied at 669 and 778 rpm. The measured performance of the mini power thresher was feeding capacity, threshing capacity, cleanliness, percentage of grain not threshed, percentage of scattered grain, threshing efficiency, percentage of yield loss, fuel consumption, and increase in cracked grain. The test sample used was IR 64 rice. Based on the test results, the rotational speed of the thresher cylinder has a very significant effect on the threshing performance of the mini power thresher. Changes in threshing cylinder rotational speed from 669 rpm to 778 rpm can improve the performance of the mini-power thresher in terms of feed capacity, threshing capacity, threshing cleanliness, percentage of grain scattered, and percentage of yield loss. The decrease in performance is found in fuel consumption and the percentage of cracked grain. The variables that relatively unchanged were the percentage of grain not threshed and threshing efficiency. The operation of this mini power thresher is recommended at a thresher cylinder rotational speed of 778 rpm.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0100.004

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 head0.214
Teacher spread0.203 · 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

Citations3
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueJurnal Engine Energi Manufaktur dan MaterialSame topicManagement and Optimization TechniquesFrench-language works237,207