Pemetaan Tiga Dimensi Gua Aul Di Desa Cikupa-Ciamis, Jawa Barat Menggunakan Terrestrial Laser Scanner (TLS)
Bibliographic record
Abstract
This study examines the potential of Terrestrial Laser Scanner (TLS) technology in documenting and understanding the characteristics of cave spaces as part of geospatial and cultural heritage. The study was conducted in Aul Cave, West Java, as part of efforts to preserve and interpret underground spaces through a digital spatial approach. Using the Trimble X7 TLS, this research produced a comprehensive three-dimensional representation of the cave structure, including its geometric shape and surface characteristics. The analysis focused on the differences in material properties between natural elements such as stalactites and rock walls, and artificial materials such as cement and paving blocks, based on laser reflection intensity values. Through this approach, the study not only provides precise visual documentation but also opens up possibilities for interpreting the function of cave spaces and indications of past human activity. The results of the study show that TLS is not only effective as a documentation tool but also relevant as a spatial analysis method in archaeological, geospatial, and cultural heritage preservation studies. This approach is expected to encourage broader utilization of digital technology in spatial-based research and the preservation of underground cultural heritage.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".