Blending Science Knowledge and AI Gaming Techniques for Experiential Learning
Bibliographic record
Abstract
This paper addresses the scientific, design and experiential learning issues in creating an extremely realistic 3D interactive of a wild beluga whale pod for a major aquarium that is situated next to a group of real beluga whales in an integrated marine mammal exhibit. The Virtual Beluga Interactive was conceived to better immerse and engage visitors in complicated educational concepts about the life of wild belugas compared to what is typically possible via wall signage or a video display, thereby allowing them to interactively experience wild whale behavior and hopefully have deeper insights into the life of beluga whales. The gaming simulation is specifically informed by research data from live belugas, (e.g. voice recordings\ntied to mother/calf behavior) and from interviews with marine mammal scientists and education staff at the Vancouver Aquarium. The collaborative user interface allows visitors to engage in educational "what-if" scenarios of wild beluga emergent behavior using techniques from advanced gaming systems, such as physically based animation, real-time photo-realistic rendering, and artificial intelligence algorithms.
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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 | 0.002 | 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 | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".