Bibliographic record
Abstract
Kualitas dari koordinat titik-titik dalam suatu jaringan yang diperoleh dengan survey GPS secara umum akan tergantung pada empat faktor yaitu : ketelitian data yang digunakan, geometri pengamatan, strategi pengamatan yang digunakan, dan strategi pengolahan data yang diterapkan. Geometri pengamatan sendiri merupakan kombinasi dari geometri jaringan dan geometri satelit. Dalam makalah ini akan dibahas pengaruh dari faktor geometri jaringan terhadap kualitas koordinat yang diperoleh dari hitung perataan jaringan GPS. Dalam hal ini parameter dari geometri jaringan yang akan ditinjau pengaruhnya terhadap ketelitian survey GPS adalah : jumlah dan distribusi dari titik tetap (titik kontrol), jumlah baseline dalam satu loop, serta konektivitas titik (jumlah baseline yang terikat ke suatu titik). Pembahasan akan didasarkan pada hasil-hasil yang diperoleh dari pengolahan data jaringan GPS Orde-3 Badan Pertanahan Nasional (BPN) di daerah Purwodadi dan Wates. Makalah akan diakhiri dengan beberapa catatan penutup.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.032 | 0.013 |
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".