Study of the Characteristics of Kutai Kertanegara Local Quarry Stone for Self-Compacting Concrete Production
Bibliographic record
Abstract
Classification, abrasion, and compressive strength conditions of rocks can affect the quality of concrete and potentially cause damage/failure of building structures.This local rock research is needed for utilization as coarse aggregate in concrete mixtures.Rocks are located in 4 quarries in Kutai Kertanegara Regency.Each quarry was identified by taking rocks in the form of boulders and coarse aggregates.Rock samples were observed visually from a geological perspective and tested for physical and mechanical properties.Rock compressive strength test with cube-shaped samples measuring 5 x 5 x 5 cm.The compressive strength value of 50 MPa was continued to make high-quality SCC concrete samples in cylinders measuring 10 x 20 cm for each age of 3, 7, 14, 21, 28, 56, and 90 days.Then, the rock classification was obtained, including limestone, and it had many cracks.The average rock abrasion value was obtained at less than 40%.The compressive strength of the rock at location KM.45 was 64.89 MPa, KM.40 obtained 42.05 MPa, while other places did not meet the minimum value.The 28-day concrete compressive strength was obtained 28.662 MPa from the KM.45 location and 23.884 MPa from the KM.40 location.The development of concrete strength according to age shows a trend that does not increase and fluctuates.Compared to concrete, it generally shows a trend that continues to increase according to age.This condition indicates that the limestone has uneven or inconsistent strength, so it is unsuitable for high-quality concrete use.This information is expected to be a guideline for using local rocks.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".