Field Soil Density Analysis Using the Sand Cone Method on the Segayam–Lebak Gedong Road Improvement Project, Ogan Ilir Regency
Bibliographic record
Abstract
Road infrastructure is crucial for the economy's smooth functioning, but its current state is largely due to natural and human factors, leading to increased traffic. Quality control is essential in road construction planning, focusing on aggregates, subgrade, and subbase layers. Factors such as water content, soil type, and compacted soil can affect soil density. A dense sub-base layer provides good bearing capacity, strengthening road construction. Improving road infrastructure is essential for maintaining the economy's lifeblood, supporting people's movement and influencing distribution and logistics activities. With the development of cities and technological advancements, national roads have grown, passing through provincial capitals and regency/city capitals. The Ogan Ilir Regency Government, through the Public Works and Housing Office, is working to meet community needs in road infrastructure, particularly in rural areas. However, many road conditions in Ogan Ilir Regency still need repairs and improvements. Road improvement in Ogan Ilir Regency should be carried out using good methods and optimal supervision. The sand cone method, which employs Ottawa sand as a parameter for soil density, is used to inspect the field density of the compacted soil layer or pavement layer.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".