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Record W3113035271 · doi:10.17762/de.vi.966

Effects of Hot Spring Tourism on Human Health

2020· article· en· W3113035271 on OpenAlexvenueno aff
Yaming Wang Qi Yu

Bibliographic record

VenueDesign Engineering · 2020
Typearticle
Languageen
FieldHealth Professions
TopicTherapeutic Uses of Natural Elements
Canadian institutionsnot available
Fundersnot available
KeywordsHot springTourismSpring (device)BathingProduct (mathematics)Human healthEcotourismBusinessMedicinePolitical scienceEnvironmental healthEngineeringMechanical engineering

Abstract

fetched live from OpenAlex

Hot spring tourism is a tourism product based on experiencing the culture of hot spring baths. Unlike conventional tourism products, it offers multiple aspects such as cultural experience and physical and mental relaxation. It has attracted increasing attention as it meets the high demand of tourists for physical and mental well-being due to the stressful and competitive lifestyle in the modern society. The fundamental value of spring tourism lies in its influence on aspects such as health care and disease treatment. To provide a comprehensive understanding of the health values of hot spring tourism, this paper has reviewed the effects of hot spring on human health from the perspectives of heat and trace its elements and functions such as bathing. This paper aims to deepen the understanding of the relationship between hot spring tourism and human health, thereby providing a theoretical reference for the future development of hot spring tourism.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.116
GPT teacher head0.404
Teacher spread0.289 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations1
Published2020
Admission routes1
Has abstractyes

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