Analisis Kurva Lengkung Debit Sungai Martapura pada Pos Duga Air Gudang Tengah, Kecamatan Sungai Tabuk, Kabupaten Banjar Provinsi Kalimantan Selatan
Bibliographic record
Abstract
bermuara ke Sungai Barito, beberapa tahun terakhir banyak kejadian banjir di daerah aliran sungai (DAS) dan salah satu nya sub DAS Martapura.Kejadian banjir dapat dimitigasi dengan updating lengkung debit secara berkala.Tujuan penelitian ini adalah untuk menganalisis lengkung debit pada Sungai Martapura.Metode yang digunakan adalah metode linear dengan persamaan linier satu peubah, eksponensial, logaritmik, polinomial, dan power dari lengkung debit.Kurva yang menggambarkan hubungan antara tinggi muka air (TMA) dan debit dilakukan dengan cara menggunakan aplikasi Microsoft Excel.Dari hasil perbandingan metode didapatkan nilai koefisien korelasi (r) adalah kuat dengan nilai 0,5 < r ≤ 0,75.Selain itu juga dilakukan uji dengan Root Mean Square Error (RMSE).Persamaan
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".