Strategi Pengelolaan DAS Way Garuntang Kota Bandar Lampung melalui Pendekatan Non Struktural
Bibliographic record
Abstract
Abstract. Watershed is one type of common pool resource determined by hydrological relationships where optimal management requires coordination in the use of resources by all users. This research was conducted in the Way Garuntang watershed in Bandar Lampung City where watershed management needs a non-structural management strategy. The data analysis method used in this data analysis is using methods, descriptive, qualitative and quantitative analysis based on phenomena that occur in the field, as well as case studies that have occurred and can identify, assess, evaluate the level of problems of a watershed. Non-structural strategy is a very effective and efficient way to solve flooding problems because this approach is an approach to the surrounding watershed. For a non-structural approach in the form of a proposal to synchronize spatial policies that are crossed by the watershed. Abstrak. Daerah Aliran Sungai merupakan salah satu jenis sumber daya common pool resource yang ditentukan oleh hubungan hidrologi di mana pengelolaan yang optimal memerlukan koordinasi dalam penggunaan sumber daya oleh semua pengguna. Pada penelitian ini dilakukan pada DAS Way Garuntang di Kota Bandar Lampung yang mana pengelolaan DAS perlu adanya strategi pengelolaan non struktural. Adapun metode analisis data yang digunakan dalam analisis data kali ini yaitu menggunakan metode, analisis deskriptif, kualitatif dan kuantitatif yang berdasarkan pada fenomena yang terjadi di lapangan, juga studi kasus yang telah terjadi dan dapat mengidentifikasi, menilai, mengevaluasi tingkat permasalahan suatu DAS. Strategi Non Struktural merupakan menyelesaikan masalah banjir yang sangat efektif dan efisien karena pada pendekatan ini merupakan pendekatan kepada sekitar DAS. Untuk pendekatan secara non strukturalnya berupa usulan sinkronisasi kebijakan tata ruang yang dilintasi DAS.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".