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Record W7055672495

DAN Annual Diving Report 2017 Edition: A Report on 2015 Diving Fatalities, Injuries, and Incidents

2018· article· en· W7055672495 on OpenAlexaboutno aff

Bibliographic record

VenueeSpace (Curtin University) · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
Fundersnot available
KeywordsScuba divingDecompression sicknessOccupational safety and healthRecreationPoison controlInjury preventionCase fatality rateSuicide prevention
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.631
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.000

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.

Opus teacher head0.012
GPT teacher head0.259
Teacher spread0.247 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2018
Admission routes1
Has abstractyes

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