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Record W4413241218 · doi:10.1079/tourism.2025.0037

Diving with Grey Nurse Sharks ( <i>Carcharias taurus</i> ): Diver Motivations and Behaviours

2025· article· en· W4413241218 on OpenAlexaff
Jaide Cambourne, Karen Hofman

Bibliographic record

VenueTourism Cases · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicIchthyology and Marine Biology
Canadian institutionsDeer Lodge Centre
Fundersnot available
KeywordsCarchariasWildlife tourismFisheryWildlifeContext (archaeology)TigerGeographyWildlife conservationEcologyBiologyArchaeologyJuvenile

Abstract

fetched live from OpenAlex

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

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score1.000

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.007
GPT teacher head0.227
Teacher spread0.221 · 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 designObservational
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
Published2025
Admission routes1
Has abstractyes

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