ANALISIS RISIKO MANAJEMEN DAN PELAKSANAAN PADA PROYEK PENINGKATAN JARINGAN IRIGASI BENDUNG CARINGIN CISOLOK
Bibliographic record
Abstract
The implementation of improving the irrigation network of the Caringin Cisolok Dam in Sukabumi Regency has a very complex level of implementation risk, because this weir has been built since 2016 and the construction was not continued due to the high level of risk faced during the implementation process. This study aims to obtain risk factors that affect the cost and time during the implementation process, risk factors will be made a risk response that must be carried out in order to minimize these risk factors and to improve the quality of achieving the final results of the work. At this research stage, identification of risk factors, analysis of risk factors and mitigation of risk factors on the Caringin Cisolok Dam project, Sukabumi Regency was carried out. The analytical method that will be used is the Saverity Index method, this concept is used to determine the value of Probability and Impact. The probability and impact values will be combined to produce the relevant risk variables. The collected data will be analyzed in the following stages: Risk identification, risk factor assessment and risk response. The results of this study indicate that the risk factor that has the greatest probability and impact is the risk factor value of evasive channel work with a risk factor value of 0.95.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".