Using 4D modelling in a university-museum research partnership
Bibliographic record
Abstract
In the field of virtual heritage, scientific 4D modelling brings together interdisciplinary expertise in an iterative process to create and revise digital models that also reflect the passage of time. Our work focusses on making this type of model serve the needs of museologists by linking various documentary sources (both text-based and iconographic) and testimonials to the 4D model. Our case study involves a former industrial site. Over the decades, various buildings in this old factory complex have been the subject of construction, demolition and redevelopment. Under a university-museum research partnership, our 4D model will be part of an interactive digital environment designed for a museum exhibit. Our goal is to maximize the flexibility of the model, which was based on i) research to find new documentary sources, ii) oral testimonies to complete the documentary record and iii) testing to make the user experience more cognitively enriching. This approach has strengthened the partnership between the museum and the university, made university research available to a wider audience and provided a modest museum with a new tool.
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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 | 0.012 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.003 | 0.023 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".