Perbandingan Gradasi Butiran Sedimen Terendap Waduk Batujai dengan Material Bahan Kerajinan Gerabah Bukit Balibe
Bibliographic record
Abstract
Sediment deposition is the main problem in the continuity of dam operations. Sustainable operation of dam can be maintained by dredging sediment deposited at the bottom of the impounding area. Because of the considerable costs and the lack of economic value of dam deposited sediment, dredging is carried out only when the serious sedimentation problem occurs. This research aims to analyse samples of sediment taken from various depths of Batujai Dam and to compare them with the pottery materials of Balibe Hill. Compositions of deposited sediment were analyzed to determine its potential as an alternative additional material used in pottery making. Comparisons were made based on the results of sieving analysis of material from both sources. The results show that the percentage of fine-grain material of Balibe Hill, especially grains with a diameter of ≤ 0.18 mm, is closer to the percentage of deposited sediment of Batujai Dam at an elevation of 92.5 masl and an elevation of 90.0 masl, while the percentage of coarse-grain material with a diameter of ≥ 3.35 mm is closer to the percentage of deposited sediment at an elevation of 87.5 masl. Gradation analysis indicates that composition of deposited sediment of Batujai Dam in sand grain classes can be engineered to some extent to make them closely identical to the composition of Balibe Hill materials so that it can be used as a mixture in pottery making.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".