Analysis of the Volume of Excavation and Stockpiling in the Bicycle Path Development Works jl. RE. Martadinata Batam Using the Elevation Difference Method with Contractor Calculations
Bibliographic record
Abstract
In eartgworks there are othe sub-works including ordinary excavation work, ordinary embankment from excavated sources, selected embankment from excavated sources, preparation of bicycle lane bodies, and cones. In this final project the author tries to make a comprasion of the volume of excavation and embankment. The outhor uses the AutoCAD Software application to a calculate the area, pours the result of the area calculation into Microsoft Excel to calculate the volume of excavations and embankments and uses the AutoCAD application to visualize excavations and embankments. Based on the result of research concudated by the author, the authors conclude that there is a significant difference in volume result from those obtained by the author using the AutoCAD application with the volume results that have been carried out by the implementing contractor. For the result of excavation volume, the volume obtained by the author is smaller than the volume calculated by the implementing contractor, namely the volume comparison value is 614.65 m3 : 756.84 m3. For the volume of embankment, the volume obtained by the author is smaller than the volume calculated by the implementing contractor, namely the comparasion value is 1.189,3875 m3 : 1.439,76 m3.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".