Analisis Penyebab dan Mekanisme Keruntuhan Lereng Sungai Konaweha, Studi Kasus Ruas Bts. Kab. Konawe Utara/Kab. Konawe – Pohara Km 29+750
Bibliographic record
Abstract
Di Sulawesi Tenggara, ruas jalan Bts. Kab. Konawe Utara / Kab. Konawe – Pohara merupakan koridor utama yang pada tahun 2019 terjadi banjir yang mengakibatkan ruas jalan tergenang dan longsor pada beberapa titik sehingga perlu dilakukan penelitian terkait penyebab dan mekanisme terjadi longsoran di Km 29+750. Penelitian dilakukan dengan merepresentasikan pengaruh perubahan tinggi muka air sungai dan tinggi muka air tanah serta pengaruh gerusan terhadap perubahan safety factor pada lereng dengan program bantu Plaxis. Dari hasil simulasi yang dilakukan, perilaku perubahan safety factor akibat pengaruh tinggi muka air sungai terjadi kenaikan nilai safety factor pada saat level muka air sungai naik. Namun akan terjadi penurunan nilai safety factor pada saat terjadi penurunan level muka air sungai secara tiba-tiba (drawdown). Pada kondisi lereng asli penurunan nilai safety factor sebesar 41%, pada lereng dengan variasi gerusan 1 penurunan nilai safety factor sebesar 44%, pada lereng dengan variasi gerusan 2 penurunan nilai safety factor sebesar 41%, pada lereng dengan variasi gerusan 3 penurunan nilai safety factor sebesar 51% sampai dengan nilai 0,93. Dari hasil analisa, lereng sungai konaweha mengalami keruntuhan terjadi pada saat penurunan level muka air sungai dengan kondisi tanah masih jenuh dan sudah terjadi gerusan 3.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".