ANALISIS PERBANDINGAN PENGUKURAN JARAK MENGGUNAKAN THEODOLITE DAN WATERPASS PADA MEDAN MIRING (SLOPE) DI AKMIL
Bibliographic record
Abstract
Pelaksanaan tugas pokok dan fungsi satuan Zeni dan Topografi TNI AD di lapangan khususnya dalam mendukung penyelesaian tugas Operasi Militer Selain Perang (OMSP) seperti pemetaan lahan, salah satunya diperlukan metode pengukuran efisien dengan kapasitas alat sesuai kondisi lapangan penugasan. Lokasi penelitian terletak di Area Gedung M. Lily Rochly Akmil menggunakan referensi pengujian metode Waterpassing yang dilakukan pada ring 1-2-3 dengan titik pangkal BM dan ujung titik pangkal yang sama. Ketelitian perhitungan dilakukan dengan perataan kuadrat terkecil untuk mendapatkan standar deviasi alat Theodolite KT 440LR Series dan Waterpass Topcon B2. Hasil pengukuran jarak vertikal (beda tinggi) dari Waterpass memiliki ketelitian lebih baik daripada Theodolite, dimana kesalahan penutup tinggi Waterpass yakni 4 mm (ring 1), 9 mm (ring 2) dan 10 mm (ring 3). Sementara pada Theodolite yakni 7 mm (ring 1), 11 mm (ring 2) dan 14 mm (ring 3). Pada Waterpass standard deviasi adalah 0,02 mm, sedangkan standard deviasi pada Theodolite yakni 0,03 mm. Waktu pengukuran Theodolite terbukti lebih efisien dibandingkan dengan Waterpass, dimana waktu pengukurannya 1/3 kali lebih pendek dari waktu pengukuran alat Waterpass.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".