Enhancing archaeological knowledge dissemination: the pivotal role of digital representation and BIM interoperability for preservation, FEA, and XR of Villa dei Quintili in Rome
Bibliographic record
Abstract
This research delves into the profound significance of the Science of Representation, demonstrating its pivotal role in crafting semantic and interoperable analysis. The adept application of these ‘mediums’ could represent a primary shift for future generations in how information is conceptualized, interpreted, represented, and communicated, ultimately fostering a deeper level of understanding and collaboration among professionals in the field. In this context, the convergence of digital representation, knowledge-driven semantic refinement, and techniques for intricate model conversion could assume a foundational role. Given these paramount considerations, the study emphasizes the urgency of establishing interoperable procedures and cultivating a comprehensive understanding of digital representation as a crucial informational medium. The results served as vessels for disseminating archaeological knowledge and as powerful tools for dissecting the structural intricacies of ancient environments. To achieve this, a highly specialized cognitive process was required to elucidate both the tangible and intangible elements within the ontological framework. The introduction of the interoperable approach at the archaeological site of Villa dei Quintili in Rome served as a prime example of the critical function of digital models able to interpret, disseminate, and preserve our cultural heritage accurately. It proved instrumental, both theoretically and practically, in transferring geometries and information for diverse analytical purposes, significantly enhancing their effectiveness through a thoughtfully selected range of exchange formats for preservation and structural analysis. Finally, the study introduces a methodology for translating digital models into advanced mediums, including extended reality (XR), pushing the boundaries of heritage preservation in the digital age. DOI: https://doi.org/10.20365/disegnarecon.30.2023.25
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".