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Record W4396230899 · doi:10.31328/js.v7i1.5583

Optimalisasi Laboratorium Terpadu Guna Mendukung Kuliah Penelitian dan Kuliah Pengabdian Kepada Masyarakat Dengan Introduksi PIOS-RT

2024· article· id· W4396230899 on OpenAlexaff
Masyhuri Machfudz, Nurhidayati Nurhidayati

Bibliographic record

VenueJURNAL APLIKASI DAN INOVASI IPTEKS SOLIDITAS (J-SOLID) · 2024
Typearticle
Languageid
FieldBusiness, Management and Accounting
TopicManagement and Optimization Techniques
Canadian institutionsWiLAN (Canada)
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

Tujuan pengabdian ini adalah (i) penataan administrasi dan (ii) program aksi pendampingan melakukan demoplot pada tanaman singkong, dan cabe melalui program kuliah pengabdian kepada masyarakat (KPM) mahasiswa Universitas Widyagama (UWG) Malang 2024 melalui ‘prototipe iptek olah sampah rumah tangga (‘PIOS-RT’)’. Metode yang dipakai adalah ekperimen dengan analisis before-after (BA).Hasil pengabdian menjukkan (i) penataan administrasi dilakukan secara baik. Indikator baik adalah terjadinya kesepakatan (agreement) antara fihak penanggungjawab PAUD Al-Qur’an Darussalam maupun Direktur Laboratorium terpadu (integrated laboratory, iL). Hal ini dapat dipakai sebagai ukuran (measure) ketercapaiannya Tingkat optimal dan (ii) program aksi pendampingan melakukan demoplot pada tanaman singkong, dan cabe melalui program kuliah pengabdian kepada masyarakat (KPM) mahasiswa Universitas Widyagama (UWG) Malang 2024. Namun demikian kelemahan pelaksanaan program ke-2 ini adalah tidak cukupnya waktu sampai mengetahui hasil dampak penggunaan pupuk komposnya, sehingga secara informal dilakukan beberapa peserta KPM menindaklanjuti hingga kompos siap diintruduksi pada singkong dan cabe.

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, Scholarly communication, 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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.704
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0030.003
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.006
Science and technology studies0.0020.001
Scholarly communication0.0120.008
Open science0.0030.002
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 head0.015
GPT teacher head0.259
Teacher spread0.244 · 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
Published2024
Admission routes1
Has abstractyes

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