PENYULUHAN PENGGUNAAN PETA DESA MENGGUNAKAN UNMANNED AERIAL VEHICLE (UAV) DI KECAMATAN CIMENYAN, KABUPATEN BANDUNG
Bibliographic record
Abstract
Creating a village map is essential for regional development planning. However, it can be costly and time-consuming. The purpose of community service is to map the area around Mandalamekar Village, Cimenyan District, Bandung Regency, as one of the supporting documents for village planning based on disaster mitigation. The scope of this community service activity includes field data acquisition, such as creating flight paths and aerial photography using UAV vehicles, as well as data processing, including ortho mosaic, digitizing maps, and spatial analysis. The community service activity will produce an Aerial Photo Image Map and Land Use Map of Mandalamekar Village, both of which will be created at a scale of 1:5000. The output from the Community Service activity can be utilized for village development planning based on disaster mitigation. Furthermore, training on drone operation and processing has been conducted with the expectation that employees of Mandala Mekar Village can utilize drone technology. The feedback from the survey suggests that the Community Service activity is aligned with the users' requirements and has the potential to offer solutions for Mandala Mekar Village.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".