A Review of Equipment Contribution among Recreational Scuba Divers
Bibliographic record
Abstract
Scuba diving is one of the most popular activities that involve risks with nature. It can lead to major injuries or even cause deaths. The rate of exposure to fatality among scuba divers has become a major concern. The number of fatalities among the divers in the United States (U.S.) and Canada is in between 80 to 100 per year. The statistic also shows over 16 fatalities rate among the divers in the U.S. and Canada for every 100,000 recreational divers per year. There are three countries with a high number of deaths among the divers in Asia, which are Malaysia, Indonesia, and Thailand. The statistic shows that these three countries have an increasing number of accidents in scuba diving activities. The upshot rate of fatalities should not be neglected and needed a critical emphasis. This paper aims to propose a framework that shows the effect of diving equipment on accidents among scuba divers. A review of previous studies was conducted to meet the objective. A previous study indicated that diving equipment had a relationship with human error in the scuba diving activity. Diving equipment condition can be measured based on equipment malfunction, faults, and misuse. Diving equipment can be one of the factors that lead to accidents among divers. For scuba diving, divers need to know the equipment before continuing with the activities to avoid any undesired situation. A direct relationship was proposed to find the effect of diving equipment and accidents. The study will contribute to the tourism sector for marine tourism’s sustainability in reducing fatality accidents among recreational divers.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".