PENGARUH VARIASI JENIS MATERIAL FILLER PADA CAMPURAN ASPAL BETON TERHADAP PARAMETER MARSHALL
Bibliographic record
Abstract
Berdasarkan hasil penelitian yang telah dilakukan di Laboratorium Teknik Sipil yangbertempat di Bukit Indah, maka Kadar Aspal Optimum (KAO) yang digunakan adalah 6,5%untuk campuran aspal beton AC-WC dengan menggunakan abu sekam padi, serbuk kaca danbatu apung sebagai bahan filler modifikasi pengganti filler standar menurut spesifikasiDepkimpraswil (2002). Dari hasil pengujian parameter Marshall menunjukkan bahwapenggunaan batu apung dapat menghasilkan nilai stabilitas tertinggi yaitu 855 kg dari padapenggunaan abu sekam padi dengan hasil 845 dan serbuk kaca yang menghasilkan 830, untuknilai flow tertinggi pada penggunaan serbuk kaca yaitu 3,65 mm. Sedangkan nilai MQ tertinggidihasilkan pada penggunaan abu sekam padi yang mampu menghasilkan nilai MQ 270kg/mm. Penggunaan abu sekam padi, serbuk kaca dan batu apung dapat digunakan untukmenggantikan filler standar dalam campuran aspal beton AC-WC menurut SpesifikasiDepartemen Permukiman Prasarana Wilayah 2002.
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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 | 0.001 |
| 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) | 0.008 | 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".