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Record W3178442531 · doi:10.32672/jse.v6i3.3047

Analisis Indeks Kebutuhan Lahan dan Biaya dari Perencanaan IPAL Terpadu di Kawasan Aerocity X

2021· article· en· W3178442531 on OpenAlexaff
Adryan Lukman Indira, Didin Agustian Permadi, Etih Hartati

Bibliographic record

VenueJurnal Serambi Engineering · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicCoastal Management and Development
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsWastewaterEnvironmental scienceEnvironmental engineeringForestryGeography

Abstract

fetched live from OpenAlex

District Aerocity X in Kabupaten Majalengka is a commercial and industrial area that enhances economic growth in Provinsi Jawa Barat. The district with an area of 3,480 ha is integrated into the domestic sector. However, this area also has the potential to cause harm if the waste is not treated. Following PP No 142 Tahun 2015, each industrial area must provide an effective and efficient wastewater treatment plant (WWTP). This design plan begins with the analysis of issues on the study site . The data were obtained using the Aerocity X District Pre-Development Office study method . The method of designing effective alternatives for WWTP used the weighted ranking technique (WRT), each alternative was compared with two fundamental, technical and non-technical aspects. The purpose of this design plan is to analyze the index of land and cost requirements for WWTP. The yield of wastewater was 3.99 m3/s. The most effective land and the cost is complete mix-activated sludge. The result of installing the design plan requires an area of 9,446.5 m2/m3 of wastewater and a cost of Rp5,619.53x106/m3 of wastewater.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0130.003

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 head0.006
GPT teacher head0.184
Teacher spread0.179 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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