First impressions: Use of the Azoth Systems O’Dive subclavian bubble monitor on a liveaboard dive vessel
Bibliographic record
Abstract
INTRODUCTION: The Azoth Systems O'Dive bubble monitor is marketed at recreational and professional divers as a tool to improve personal diving decompression safety. We report the use of this tool during a 12-day dive trip aboard a liveaboard vessel. METHODS: Six divers were consistently monitored according to the user manual of the O'Dive system. Data were synchronised with the Azoth server whenever possible (depending on cell phone data signal). Information regarding ease of use, diver acceptance and influence on dive behaviour were recorded. RESULTS: In total, 157 dives were completely monitored over 11 diving days. Formal evaluations were only available after six days because of internet connection problems. Sixty-one dives resulted in the detection of bubbles, mostly in one diver, none of which produced any symptoms of decompression illness. CONCLUSIONS: The O'Dive system may contribute to increasing dive safety by making divers immediately aware of the potential consequences of certain types of diving behaviour. It was noted that bubble monitoring either reinforced divers in their safe diving habits or incited them to modify their dive planning. Whether this is a lasting effect is not known.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".