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Record W3046525706 · doi:10.32585/ijecs.v1i1.779

Pendampingan Perhitungan Analisa Revitalisasi Lapangan Desa Masaran Kecamatan Masaran Kabupaten Sragen

2020· article· en· W3046525706 on OpenAlexaff
Sumina Sumina, Herman Susila

Bibliographic record

VenueIJECS Indonesian Journal of Empowerment and Community Services · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicLocal Governance and Development
Canadian institutionsWiLAN (Canada)
Fundersnot available
KeywordsEngineering

Abstract

fetched live from OpenAlex

Desa Masaran merupakan salah satu desa di wilayah Kecamatan Masaran, Kabupaten Sragen, Jawa Tengah. Desa Masaran merupakan wilayah yang berada pada lokasi yang sangat strategis sebagai titik pertemuan jalur mobilisasi barang dan manusia. Desa Masaran memiliki potensi berupa lapangan desa. Aset tersebut sangat potensial karena berada pada tanah terbuka yang cukup luas di tengah wilayah yang semakin rapat. Sehingga perlu ada optimalisasi potensi tersebut agar dapat memberikan manfaat bagi masyarakat dan lingkungan. Guna mengoptimalkan dan mencapai hal tersebut maka tim pengabdian masyarakat akan melakukan pendampingan dalam hal merencanakan analisa biaya untuk merevitalisasi lapangan Desa Masaran. Berdasarkan hasil pendampingan dapat diketahui bahwa besarnya dana untuk merevitalisasi lapangan Desa Masaran adalah sebesar Rp 2.338.900.000,00. Hasil analisa biaya tersebut untuk selanjutnya dapat digunakan untuk mencairkan dana desa yang telah ada dan juga dapat berfungsi sebagai dasar dalam mencari donatur untuk menyukseskan revitalisasi lapangan tersebut.

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)0.002
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 score0.012
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0120.002

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 head0.039
GPT teacher head0.288
Teacher spread0.249 · 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
Published2020
Admission routes1
Has abstractyes

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