Diving with Grey Nurse Sharks ( <i>Carcharias taurus</i> ): Diver Motivations and Behaviours
Bibliographic record
Abstract
Summary Over the past two decades, Marine Wildlife Tourism (MWT), focusing on megafauna, such as whales, sharks, and manta rays, has become an increasingly popular activity. Such MWT, including shark diving tourism, is often heralded as a flag for shark conservation activism. The large, but placid Grey Nurse Shark ( Carcharias taurus )—sometimes known as the sand tiger or ragged-tooth shark—is an ideal species for such MWT, providing opportunities for SCUBA divers to interact with sharks without the need for protective cages. Indeed, diving with Grey Nurse Sharks (GNS) has become a popular attraction in Australia and is now a multi-million-dollar industry. In this context, this case examines the characteristics and behaviour of divers at the important aggregation site of Flat Rock, North Stradbroke Island, Queensland, Australia. Understanding diver motivations and behaviours is critical in informing best practice shark diving operations, including the design and delivery of communications that influence human behaviours, minimise wildlife impacts, and enhance pro-conservation outcomes. Information © The Authors 2025
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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 | 0.000 |
| 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) | 0.001 | 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".