Pemilihan Ground Motion Model di Wilayah Jawa Menggunakan Data Percepatan Tanah Tahun 2010-2021
Bibliographic record
Abstract
Wilayah Jawa memiliki aktivitas seismik yang tinggi akibat terletak di zona subduksi serta keberadaan beberapa sesar di daratan. Wilayah Jawa juga memiliki jumlah penduduk dan pertumbuhan infrastruktur tertinggi, sehingga upaya mitigasi harus dilakukan untuk mengurangi risiko gempa. Langkah paling penting untuk mengatasi hal tersebut adalah dengan memiliki ground motion model (GMM) yang sesuai dengan wilayah Jawa. Pada studi ini dibuat set data strong motion yang terdiri dari Peak Ground Acceleration (PGA), parameter gempa, dan kondisi situs pada periode 2010-2021. Data tersebut kemudian digunakan untuk memilih GMM yang memiliki residual paling rendah. Analisis dilakukan berdasarkan grafik histogram residual dan nilai standar deviasi untuk mendapatkan model yang terpercaya. Hasil penelitian menunjukkan beberapa model memiliki performa yang baik dalam memerkirakan nilai PGA. Model yang terpilih dapat digunakan untuk memerkirakan dampak gempa subduksi maupun crustal wilayah Jawa di masa depan.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".