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Record W4312459948 · doi:10.20527/jernih.v5i1.1422

PERENCANAAN BANK SAMPAH DI DESA SUNGAI SIPAI KABUPATEN BANJAR

2022· article· id· W4312459948 on OpenAlexaff
Nadiar Iswanto, Muhammad Firmansyah

Bibliographic record

VenueJernih Jurnal Tugas Akhir Mahasiswa · 2022
Typearticle
Languageid
FieldEnvironmental Science
TopicWaste Management and Recycling
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsPhysicsForestryGeography

Abstract

fetched live from OpenAlex

Bank Sampah merupakan satu upaya dalam memanfaatkan potensi timbulan sampah yang bernilai ekonomi di Desa Sungai Sipai, Kabupaten Banjar. Bank sampah dinilai merupakan fasilitas pengelolaan sampah dengan konsep 3R yang efektif menyelesaikan masalah persampahan. Penelitian ini dilakukan untuk mengindentifikasi potensi timbulan sampah yang bernilai ekonomi dan merencanakan bank sampah di Desa Sungai Sipai. Diketahuinya potensi timbulan sampah yang ada di Desa Sungai Sipai, khususnya sampah yang memiliki nilai ekonomi. Desa Sungai Sipai memiliki potensi timbulan sampah yang bernilai ekonomi sebesar 1.704,7 kg/hari dengan nilai ekonomi mencapai Rp 1.474.774/hari. Hal tersebut menunjukan perlu adanya pengelolaan sampah yang bernilai ekonomi di Desa Sungai Sipai melalui perencanaan bank sampah. Perencanaan Bank Sampah yang dilakukan di Desa Sungai Sipai meliputi pengelolaan sampah, fasilitas bank sampah dan tata kelola bank sampah. Selain itu disertakan pula rancangan bangunan bank sampah dan rencana anggaran biaya perencanaan bank sampah sebagai upaya realisasi perencanaan ini.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.029
Threshold uncertainty score0.087

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0260.006

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 head0.019
GPT teacher head0.228
Teacher spread0.209 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2022
Admission routes1
Has abstractyes

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