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Record W4306968942 · doi:10.18280/ijsdp.170604

Horizontal Trash Rack Diverter Trash (HTDT) to Minimize Trash Clogging at the Intake of Micro-Hydro Power Plant

2022· article· en· W4306968942 on OpenAlexvenueno aff
Masrur Alatas, Etty Susilowati, Maria Theresia Sri Budiastuti, Totok Gunawan, Prabang Setyono, Sunarto Sunarto

Bibliographic record

VenueInternational Journal of Sustainable Development and Planning · 2022
Typearticle
Languageen
FieldEngineering
TopicBelt Conveyor Systems Engineering
Canadian institutionsnot available
FundersUniversitas Gadjah MadaUniversitas Sebelas Maret
KeywordsCloggingRackChannel (broadcasting)Environmental scienceFlow (mathematics)Power (physics)Waste managementEnvironmental engineeringEngineeringMechanical engineeringElectrical engineeringMathematics

Abstract

fetched live from OpenAlex

Clogging of Trash at the Micro Hydro-Power Plant can reduce the discharge, head, and micro-hydro production. Trash racks are currently less efficient in solving Trash clogging, so it needs appropriate technology innovation with the Horizontal Trash rack Diverter Trash (HTDT) which functions to get rid of or divert Trash. Diversion of Trash as well as increasing and stabilizing the discharge is important so that the innovation of adding a flow steering valve is needed (HTDT+V). The results of the research at β20° is the most optimal angle, the highest speed at the intake channel Cm4 V 0.7 m/s and HTDT + V β20° V 0.8 m/s occurs an increase in speed. Trash paste time β20° t 2.76 seconds, faster than the angle β0°, β5°, β30° Trash paste time t3.5 seconds. The HTDT+V installation increases the inflow velocity (V) in the intake channel by 60% and increases the discharge (Q) by 50%.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.018

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)0.0050.001

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 head0.008
GPT teacher head0.199
Teacher spread0.191 · 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

Citations5
Published2022
Admission routes1
Has abstractyes

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