Multi-Resolution Digital 3D Imaging System Applied to the Recording of Grotto Sites: the Case of the Grotta dei Cervi
Bibliographic record
Abstract
The Grotta dei Cervi is a complex and fragile Neolithic cave where human presence left a large number of unique pictographs and petroglyphs. Detailed documentation necessitates recording it at different levels of details or spatial resolutions. A general approach would entail a combination of 3D data from different 3D sensors and information from different sources in order to meet set resolution targets. We used a prototype multi-resolution 3D laser imaging scanner that allowed acquiring the shape information of the three main chambers with a spatial resolution that improves with shorter standoffs. The system can record 3D data at a camera-to-object distance which ranges from 0.5 m to 10 m. At a standoff of 0.75 m, it provides a depth uncertainty of 0.08 mm and an optical lateral resolution of 0.2 mm on actual rock surfaces. This paper presents the project and the results obtained. The 10-day long visit into the Grotto generated more that 100 GB of 2D and 3D data that requires the development of new tools for modelling and managing the archive.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".