Digital Reconstruction as a means of understanding a building’s history - Case studies of a multilayer prototype
Bibliographic record
Abstract
The experiments presented in this paper are situated at the crossroads of two fields: the understanding and communication of history to students and the field of Information and Communication Technologies (ICT). More specifically, we aim to propose to students, ways of transferring information about lifestyles and techniques linked to the construction methods used in the past and which are present in ancient sites. It is not merely a question of proposing experiments for managing an inventory of knowledge such as that summarized in historical texts, but rather a means for understanding it: How do we communicate the invisible? How do we make visible what we cannot see but that we can imagine lies beneath the ruins of ancient sites? Lastly, how do we propose new approaches in the transferring of these historic skills and lifestyles? Such are the questions that the students’ experiments will attempt to answer while using computers as cognitive tools. In this case, these cognitive tools are designated as “multilayer prototypes” which aim to develop a dynamic virtual history space through augmented reality.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".