Dari Data ke Tindakan: Echosounder Singlebeam untuk Pemetaan dan Keamanan Kapal Wisata
Bibliographic record
Abstract
Sungai memainkan peran penting sebagai jalur transportasi dan sumber daya alam bagi masyarakat sekitar, namun masalah pendangkalan sungai semakin mendesak dan mengancam keselamatan kapal wisata serta ekonomi lokal. Pengabdian ini bertujuan untuk meningkatkan keselamatan navigasi kapal wisata melalui pemetaan kedalaman sungai menggunakan Echosounder Singlebeam Garmin GPS Map 585 Plus di Daerah Aliran Sungai (DAS) Kahayan, Kota Palangka Raya. Metode yang diterapkan menggantikan pengukuran tradisional dengan teknologi modern, yang meliputi perencanaan, pelaksanaan, dan evaluasi. Tahap perencanaan mencakup observasi lapangan dan koordinasi dengan pelaku usaha kapal wisata untuk mengidentifikasi area yang mengalami pendangkalan. Data kedalaman yang diperoleh akan dianalisis menggunakan perangkat lunak Sistem Informasi Geografis (GIS) untuk menghasilkan peta kedalaman yang informatif. Pelaksanaan mencakup sosialisasi dan pelatihan bagi komunitas kapal wisata mengenai penggunaan peta dalam navigasi. Evaluasi dilakukan untuk menilai kelebihan dan kekurangan pemetaan ini. Dengan melibatkan masyarakat, diharapkan mereka dapat memahami pentingnya data kedalaman sungai dan memanfaatkan peta secara efektif, sehingga meningkatkan keselamatan dan keberlanjutan operasional di sektor pariwisata.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.047 | 0.018 |
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".