Bibliographic record
Abstract
BACKGROUND: Mermaiding - swimming with a leg-covering monofin mimicking the tail of a mermaid - is an emerging aquatic activity, which has gained a marked popularity over the last few years. However, no study so far has documented the potential health issues or risks of injuries related to this practice. MATERIALS AND METHODS: This study surveyed professional mermaids cumulating an estimated total of 19,147 h of in-water mermaiding, regarding their health issues and injuries. While mermaiding bears some risks, the occurrence of problematic conditions appears limited. Interestingly, the profile of health issues experienced by professional mermaids is unique and specific, and clearly different from both professional swimmers and surfers. RESULTS: Self-reported health issues related to mermaiding could be divided into issues specifically related to mermaiding activities (ear issues, reported by 87.5% of the respondents; sea life encounters, 50%; cold-related issues, 37.5%; compromised access to air, 25%), issues related to the tail and fins (back pain, 50%; lower limbs issues, 37.5%), and issues related to water quality (eye issues, 25%; waterborne diseases, 12.5%). Clear differences appear between professional and recreational mermaiding activities. CONCLUSIONS: The results presented here will help to build safer conditions for mermaiding activities and to develop adapted responses from health specialists to help this unique yet growing population of aquatic performers and athletes.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".