Understanding Diver Motivation and Specialization for Improved Scuba Management
Bibliographic record
Abstract
This study explores diver motivations in the Azores in relationship to demographic variables and level of specialization, based on a survey of 425 divers in five of the nine islands. Using cluster and principal components analysis, four diver clusters were distinguished: socializers, shark and manta divers, biodiversity seekers, and explorer divers. Social aspects of diving were important to both generalists and specialists, and the importance of underwater fauna did not increase with specialization. Divers’ cultural background affected their motivations. the Azores archipelago, an emerging nontropical diving destination, featuring diving with large iconic species including sharks and manta rays, has a higher proportion of specialized divers than reported in other diving destinations and may receive divers displaced from increasingly degraded tropical reefs. Findings highlight the importance of understanding diver motivations and developing diver awareness programs at all stages of specialization, as well as an integrated management strategy.
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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 | 0.001 | 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) | 0.000 | 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".