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Record W7156192528 · doi:10.35718/sepakat.v4i5.8481573

KOLABORASI MULTIDISIPLIN UNTUK PENGEMBANGAN LINGKUNGAN RT: PEMETAAN, DAN INFRASTRUKTUR

2025· article· W7156192528 on OpenAlexaff
Elin Diyah Syafitri, Azhari Thesarudin, Gunawan Nababan3, Aldi Novrisal Ramadhan, Desta Septiyani, Elshadai Cantika, Syarifah Nabila Qoidah, Jeremy Sallomo Salinding, Fairuz Insyirah

Bibliographic record

VenueSeminar Nasional Pengabdian Kepada Masyarakat (SEPAKAT) · 2025
Typearticle
Language
FieldEnvironmental Science
TopicWaste Management and Recycling
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsHandicraft

Abstract

fetched live from OpenAlex

Kegiatan ini dilaksanakan di RT 38 Kelurahan Gunung Samarinda, Balikpapan Utara, sebagai bentuk pengabdian masyarakat berbasis kolaborasi multidisiplin. Kegiatan ini merespons isu lingkungan seperti belum adanya peta administrasi wilayah, fasilitas umum yang tidak memadai, dan sistem pengelolaan sampah yang belum optimal. Empat fokus program utama dirancang, meliputi: pembuatan peta wilayah, perencanaan desain posyandu dan pos kamling beserta RAB, pemasangan papan nama jalan dan gang, serta pembentukan Bank Sampah berbasis partisipasi warga. Metode pelaksanaan terbagi dalam tiga tahap: persiapan, pelaksanaan, dan evaluasi. Partisipasi aktif warga dan mitra RT sangat berperan dalam kelancaran kegiatan. Meskipun menghadapi beberapa kendala teknis dan administratif, program berjalan dengan baik dan menghasilkan luaran nyata bagi masyarakat. Kegiatan ini tidak hanya memberikan dampak fisik bagi lingkungan, tetapi juga memperkuat keterlibatan warga dalam pembangunan wilayah. Pengabdian masyarakat ini diharapkan menjadi model pengembangan kawasan berbasis kolaborasi antarbidang keilmuan dan partisipasi masyarakat.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.390
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.004
Science and technology studies0.0030.002
Scholarly communication0.0010.002
Open science0.0030.004
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0030.003

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 head0.008
GPT teacher head0.249
Teacher spread0.241 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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
Published2025
Admission routes1
Has abstractyes

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