On 3D Dimension: Study cases for Archaeological sites
Bibliographic record
Abstract
Abstract. For more than a century the tridimensional vision has been of interest for scientists and users in several fields of application. The mathematical bases have remained substantially unchanged but only the new technologies have allowed us to make the vision really impressive. Photography opens new frontiers and has enriched of physical, mathematical, chemical, informatical and topographic notions by making the images so real to make the observer fully immersed into the represented scene. By means of active googless the 3D digital technique, commonly used for video games, makes possible animations without limitations in the dimension of the images thanks to the improved performances of the graphic processor units and related hardware components. In this paper we illustrate an experience made by the students of the MSc'degree course of Topography, active at the University of Cassino and Southern Lazio, in which the photography has been applied as an innovative technique for the surveying of cultural heritage. The tests foresee the use of traditional techniques of survey with 3D digital images and use of GPS sensors. The ultimate objective of our experience is the insertion in the web, allowing us the visualization of the 3D images equipped with all data. In conclusion these new methods of survey allow for the fusion of extremely different techniques, in such an impressive way to make them inseparable and justifying the origin of the neologism "Geomatics" coined at the Laval University (Canada) during the eighties.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".