PERENCANAAN SISTEM JARINGAN DISTRIBUSI SISTEM PENYEDIAAN AIR MINUM PEDESAAN (SPAMDES) DI DESA PEDAWA KECAMATAN BANJAR BULELENG
Bibliographic record
Abstract
Water is a very important natural resource for mankind. Pedawa Village of Banjar Subdistrict located in the southern part of Buleleng Regency with an area of 16.68 km2 which has limited clean water services. Where the level of SPAMDES service which is only 29% or 1,672 people out of 5,733 people causes the fulfillment of clean water in Pedawa Village is still low, there are 4 springs used in SPAM Pedawa Village but not all communities get water, so it is necessary to plan an optimal rural drinking water supply network system (SPAMDES). The planning of this distribution network system is assisted by WaterNet where the software uses energy potential to see the extent to which water from the spring can flow through the distribution network.. The data required in this planning are elevation, discharge and coordinate point to calculate energy loss, pipe length and pump head and the number of residents of the last 6 years for projected population growth for 20 years. The results of projected water needs in the service area were obtained for Lambo Reservoir 54 m3, Lambo2 Reservoir 99 m3, Bangkiang Sidem1 Reservoir 45 m3, Bangkiang Sidem2 Reservoir 45 m3, Reservoir Gelunggang 75 m3, Asah Reservoir 15 m3, Insakan Reservoir 9 m3 and Munduk Waban Reservoir 1.5 m3. Based on the results of network simulations with galvanized pipes and flow systems with pumps and gravity obtained results for Constant and Extended flow types on WaterNet hydraulically the network can provide services as desired on each node. Where the pipe for the transmission network is obtained a pipe diameter of 10 and 5 cm, and for the distribution network obtained a pipe diameter of 15, 10 and 5 cm
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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".