DAN Annual Diving Report 2017 Edition: A Report on 2015 Diving Fatalities, Injuries, and Incidents
Bibliographic record
Abstract
The 30th DAN Annual Diving Report presents a summary of recreational scuba diving fatalities, injuries and incidents from 2015. There were 67 US or Canadian fatalities recorded, with Florida and California accounting for almost half of all fatalities in the US. Males accounted for 4 out of 5 deaths and 90% of all deaths were aged 40 years or older. Cardiovascular issues were a contributing factor in many deaths. The Medical Department received more than 11,500 medical inquiries in 2015, including more than 3,500 emergency calls. The most common injury involved ear or sinus barotrauma and there were 250 cases of decompression sickness. The Diving Incident Reporting System (DIRS) received another 107 incident reports in 2015, most commonly from the victim of the incident. Incidents most commonly occurred on the first day of diving, and more than half the divers had been certified for less than two years. Fatality and injury data are also presented from a number of international regions. A review of the last 30 years of the Annual Diving Report was conducted. The mean annual number of US and Canadian recreational diving fatalities has fallen since 1988 from 90 to 80 per year. Age data were available for 2,267 fatalities, 80% of which were male, and Body Mass Index (BMI) data for 1,219 fatalities. Over the 30 year period, US and Canadian recreational diving fatalities steadily increased in both age and BMI.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.012 |
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".