Field validation of Tasmania's aquaculture industry bounce-diving schedules using Doppler analysis of decompression stress.
Bibliographic record
Abstract
INTRODUCTION: Tasmania's aquaculture industry produces over 40,000 tonnes of fish annually, valued at over AUD500M. Aquaculture divers perform repetitive, short-duration bounce dives in fish pens to depths up to 21 metres' sea water (msw). Past high levels of decompression illness (DCI) may have resulted from these 'yo-yo' dives. This study aimed to assess working divers, using Doppler ultrasonic bubble detection, to determine if yo-yo diving was a risk factor for DCI, determine dive profiles with acceptable risk and investigate productivity improvement. METHODS: Field data were collected from working divers during bounce diving at marine farms near Hobart, Australia. Ascent rates were less than 18 m·min⁻¹, with routine safety stops (3 min at 3 msw) during the final ascent. The Kisman-Masurel method was used to grade bubbling post dive as a means of assessing decompression stress. In accordance with Defence Research and Development Canada Toronto practice, dives were rejected as excessive risk if more than 50% of scores were over Grade 2. RESULTS: From 2002 to 2008, Doppler data were collected from 150 bounce-dive series (55 divers, 1,110 bounces). Three series of bounce profiles, characterized by in-water times, were validated: 13-15 msw, 10 bounces inside 75 min; 16-18 msw, six bounces inside 50 min; and 19-21 msw, four bounces inside 35 min. All had median bubble grades of 0. Further evaluation validated two successive series of bounces. Bubble grades were consistent with low-stress dive profiles. Bubble grades did not correlate with the number of bounces, but did correlate with ascent rate and in-water time. CONCLUSIONS: These data suggest bounce diving was not a major factor causing DCI in Tasmanian aquaculture divers. Analysis of field data has improved industry productivity by increasing the permissible number of bounces, compared to earlier empirically-derived tables, without compromising safety. The recommended Tasmanian Bounce Diving Tables provide guidance for bounce diving to a depth of 21 msw, and two successive bounce dive series in a day's diving.
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How this classification was reachedexpand
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".