Pemilihan Bangunan Pelindung terhadap Bencana Hidrometri Basah dalam Rekayasa Sumberdaya Air
Bibliographic record
Abstract
Inaccuracy in selecting the type of water resources structures as protection structures against wet hydrometric disasters means that the effects of the disaster are not reduced significantly. This situation gets worse if the placement of the protection structures is not correct. This research focuses on selecting the type and location of protection structures against wet hydrometric disasters in water resources engineering so that they work optimally. This optimization is carried out by maximizing the function of the structures as protector against hydrometric disasters, and minimizing new disasters that may arise due to the presence of these protection structures. The first stage is to identify the behavior and to predict the characteristics of water at location of the wet hydrometric disaster and its surroundings. This is done by analyzing video and aerial photos in several circumstances. The next stage is to select the appropriate type of protection structures. The final stage is to determine the location and dimensions of the protection structures with the consideration that new disasters that may arise due to the presence of the protection structures must be relatively small. Another consideration for determining the dimensions of a structure is the characteristics of the water. Determination of the location and dimensions of the protection structures are carried out using theoretical simulations. The results of research in the field show that protection structures work optimally and with relatively small dimensions and numbers, and is in accordance with theoretical estimates.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.036 | 0.006 |
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".