KOMBINASI FILLER LIMESTONE DAN ABU BATU PADA CAMPURAN LASTON LAPIS AUS MENGGUNAKAN METODE MARSHALL
Bibliographic record
Abstract
Filler digunakan untuk meningkatkan stabilitas dan kerapatan dari campuran aspal. Limestone dengan unsur utamanya kalsium dalam bentuk halus dapat meningkatkan viskositas campuran yang membuat daya lekat antar agregat menjadi tinggi. Penelitian ini menggunakan kombinasi filler antara limestone dan abu batu sebesar 0%, 25%, 50%, 75% dan 100% serta kadar aspal optimum sebesar 6,75% pada campuran laston wearing course. Pengujian dan analisis menggunakan metode marshall berupa pemeriksaan volumetric dan mekanis. Pengaruh penambahan filler limestone lebih dari 50% pada abu batu menunjukkan penurunan nilai VIM dan VMA, sedangkan VFB semakin meningkat. Nilai stabilitas dan marshall quotient dengan penambahan limestone cenderung menurun, hal ini disebabkan karena interlocking antar agregat semakin berkurang dan banyak aspal yang bisa mengisi rongga campuran. Nilai marshall quotient mengalami penurunan berarti tingkat plastisitasnya tinggi maka campuran tidak akan mudah mengalami retak. Sebaliknya, semakin banyak aspal yang mengisi rongga nilai flow cenderung meningkat. Penggunaan filler limestone lebih dari 50% pada campuran laston wearing course tidak memenuhi persyaratan karena mempunyai nilai flow di luar interval 2-4%.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".