PEMBUATAN PETA KONTUR \n \n UNTUK PERENCANAAN AREA WADUK JATIGEDE \n \nKAB. SUMEDANG \n
Bibliographic record
Abstract
Laporan ini berdasarkan hasil kegiatan selama mengikuti Program Latihan Akademik (PLA), dimana setiap mahasiswa yang mengikuti kegiatan PLA wajib menyusun dan membuat sebuah karya tulis dalam bentuk laporan sesuai dengan apa yang dilakukan mahasiswa selama kegiatan PLA berlangsung. Penulis mengikuti kegiatan PLA ini di PT. Geocal. \nSalah satu kegiatan PLA yang penulis tuangkan dalam pelaporan ini yaitu pengukuran dan pemetaan untuk perencanaan area waduk Jatigede di kabupaten Sumedang. Pengukuran dan pemetaan ini dilakukan pengambilan data secara lngsung di lapangan sehingga sampai pada proses pengolahan data. \nPada proses pengukuran dan pemetaan topografi ini penulis proses pengmbilan data mulai dari tahap persiapan sampai pada pengolahan data, metode pengukuran yang dipakai dan alat-alat yang digunakan. \nTujuan akhir dari pengukuran ini adalah mendapatkan kontur daerah untuk mendapatkan level genangan waduk. \n \n \n
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.135 | 0.039 |
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".