Penerapan Struktur Parapet dan Tanggul Tanah dalam Pengendalian Banjir Kali Jeroan Hilir di Desa Balerojo, Kab. Madiun, Jawa Timur
Bibliographic record
Abstract
Kali Jeroan merupakan salah satu sungai yang hampir setiap tahunnya terjadi curah hujan yang tinggi sehingga mengakibatkan banjir dan ketidakmampuan sungai mengaliri debit air dari hulu yang begitu besar. Banjir yang menggenangi area pemukiman yang berada di sekitar sempadan sungai setinggi 0,5 – 1,50 m dan menutupi akses jalan antar desa di daerah Balerejo, Kab. Madiun mengakibatkan aktivitas warga menjadi terhenti selama banjir. Akibat dari tata ruang yang berahli fungsi paada daerah hulu serta ilegal dalam pemanfaatan sumber daya alam sehingga regim Kali Jeroan mejadi terpengaruh. Adanya ketidakmampuan penampang sungai kali jeroan adalah salah satu penyebab banjir di beberapa titik pemukiman di daerah Balerejo. Sehingga dilakukan pembangunan infrastruktur pengendalian banjir berupa Parapet dan Tanggul Tanah di beberapa titik meluapnya banjir tersebut. Penelitian bertujuan menganalisis efektifitas pembangunan pencegah banjir terhadap banjir yang dapat dikurangi, yang ditunjukkan dengan penurunan elevasi muka air di Kali Jeroan melalui pemodelan HEC-RAS. Simulasi HEC-RAS menunjukkan hasil untuk kondisi eksisting dengan rencana debit banjir siklus 100 tahun, bahwa muka air banjir meluap dengan ketinggian antara 0,5 m hingga 1,59 m. Setelah dilakukannya peningkatan parapet dan peningkatan tanggul tanah, didapatkan elevasi muka air banjir tidak mengalami overtopping, sehingga banjir dapat diantisipasi.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.036 | 0.014 |
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".