Experiencing Belugas: Action Selection for an Interactive Aquarium Exhibit
Why this work is in the frame
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Bibliographic record
Abstract
In this article we present a case study of an action-selection system designed with adaptive techniques to create a virtual beluga aquarium exhibit. The beluga interactive exhibit uses a realistic three-dimensional simulation system, which allows the virtual belugas, in a natural pod context, to learn and alter their behavior based on contextual visitor interaction. Ethogram information on beluga behavior was incorporated into the simulation, which uses physically based systems for natural whale locomotion and water, artificial intelligence systems including modified neural networks and a reactive hierarchical action-selection mechanism to simulate real-time natural individual beluga and group behavior. The beluga’s behavioral system consists of two layers: a low-level navigation system and a high-level reaction hierarchical action-selection system. The system is designed to be run on consumer level hardware while maintaining real-time speeds.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it