Analisis Penerapan Manajemen Proyek dalam Perencanaan Instalasi Pengolahan Air Limbah (IPAL) di Perkotaan (Studi Kasus di Kawasan Tb. Simatupang)
Bibliographic record
Abstract
Urban WWTP development in the Simatupang area will integrating water life cycle, from managing waste water flow to reservoir, and then the raw water will be treated for drinking water. The most important effluent of waste water must be store first in the reservoir, and then treated for drinking water; it is possible direct converted from waste water to drinking water, however, the culture and religion of the community was not accepted. Construction of WWTP will be applied for business/mixuse area along 8 km and utilize 9 Ha land in the commercial area. The formulation of the problem of analyzing the application of project management in urban WWTP planning in the TB Simatupang area, is how the application of project management can affect the smoothness and success of the planning process for Urban WWTP development in the TB Simatupang area. In this problem formulation, the focus is placed on the effect of project management implementation on Simatupang WWTP planning. This led to research on evaluating the project management steps taken and their impact on the smoothness and success of the WWTP planning.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".