Interaction with replicas of small pieces: using complementary technologies to maximize visitors' experience
Bibliographic record
Abstract
Current technologies for digitizing artifacts allow us to create compelling virtual installations, in which visitors learn about them through playing and exploring virtual proxies. However, different technologies enhance certain types of information and preclude other usages. In this paper, we show how one can create complementary installations in order to enhance the use of available information of small artifacts. Our case study is a set of small gold artifacts at the Gold Museum in Bogota, Colombia. We collected from each piece high-resolution 3D scans at different levels of detail, high resolution images, sound, text, and contextual images. With this information, we created a traditional multimedia installation for the computer room at the Museum, a web site for remote visitors through the Internet, and finally a novel haptic and stereo display interface that allows visitors to touch, observe in stereo, locate themselves inside the Museum, and hear the sound of an artifact when it is struck with a virtual stylus. In this paper, we show how one can develop these experiences and how they complement each other. We will also present an early evaluation of their strengths and weaknesses.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| 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".