Developing WaterHCI and OceanicXV technologies for Diving
Bibliographic record
Abstract
Technical diving requires us to be fully cognizant of our interactions with advanced life support systems, our adaptation to the natural environment and our participation in complex oceanic operations. It exists in the liminal space between water, humans, and technology. We explore the future of technical, artistic, and research diving at the intersection of water and human-computer interaction (HCI), specifically through WaterHCI (Water-Human-Computer Interaction) re-configured for eXtended Reality (XR) and eXtended meta-uniVerse (XV) technical diving systems. The ocean is a live and extended immersive environment. An OceanicXR/XV is key to re-envisage beyond the so-often undifferentiated oceanic-symbolic world encountered in XR/XV to support underwater omnidirectional awareness and communication. As we dive into exploring three underwater case studies, we are highlighting the probabilities of WaterHCI and OceanicXV for the future of technical diving. Through our experimentation to reconceive XV underwater, we initiate a quest for an HCI design language that incorporates interactions uniquely aquatic. The opportunity in the marine and waterways extended infrastructure of the metaverse is equally technical and conceptual. We aim to address the gap of design for dynamic and phenomenological paradigms spanning transits between submerged, terrestrial, and aerial environments, and into virtuality and alterity from deep time to Narcosis. Building upon the XV framework, we submit an initial framework conceptualization of the OceannXV to advance global impact goals in marine technologies for life underwater.
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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 | 0.003 |
| 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 | 0.003 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".