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Record W4214747291 · doi:10.29303/jpmpi.v5i1.1302

Edukasi Tanggap Bencana Melalui Kegiatan Sosialisasi Guna Mewujudkan Masyarakat Desa Pijot Yang Tangguh

2022· article· id· W4214747291 on OpenAlexaff
Dipo Ario Kusuma, Siti Maryam Ulfa, Arni Emiza Febrianti, Rosiatul Ismi, Siti Nuriah, Nurul Zainiyah, Rina Nuranjanisa, Soraya Rosanti, Ni Komang Eva Yuniasih, Tiara Hesti Amanda, M Andara, Lalu Sumardi

Bibliographic record

VenueJurnal Pengabdian Magister Pendidikan IPA · 2022
Typearticle
Languageid
FieldComputer Science
TopicData Mining and Machine Learning Applications
Canadian institutionsWiLAN (Canada)
Fundersnot available
KeywordsPhysics

Abstract

fetched live from OpenAlex

Desa Pijot secara administratif merupakan salah satu dari 15 desa di Kecamatan Keruak, yang teletak pada radius 3 kilometer sebelah timur Ibu Kota Kecamatan Keruak. Desa Pijot memiliki luas wilayah 715 hektar, tersebar pada 8 kepala dusun, dengan jumlah penduduk di Desa Pijot sebanyak 6113 jiwa. Jumlah penduduk yang banyak dan letak daerah yang berada di pesisir pantai dengan tinggi tempat dari permukaan laut sebesar 500 mdpl dan tingkat curah hujan 491 mm. Kondisi Desa Pijot yang secara geografis, geologis, hidrologis dan demografis yang rawan terhadap bencana dengan frekuensi yang cukup tinggi, serta fakta bahwa kesadaran dan pengetahuan masyarakat yang minim dalam melakukan penanggulangan bencana. Untuk itu pengabdi merasa perlu menanamkan pemahaman dan pembelajaran khusus kepada masyarakat melalui kegiatan sosialisasi tanggap bencana darurat. Tujuan kegiatan ini agar masyarakat memiliki pengetahuan terkait penanggulangan bencana dan selalu siap siaga saat terjadi bencana yang sewaktu-waktu bisa terjadi, sehingga dapat mewujudkan masyarakat Desa Pijot yang tangguh dan tanggap bencana.

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: Empirical · Consensus signal: none
Teacher disagreement score0.052
Threshold uncertainty score0.139

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0410.004

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 head0.021
GPT teacher head0.263
Teacher spread0.242 · 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
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

Citations1
Published2022
Admission routes1
Has abstractyes

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