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Record W4390627501 · doi:10.19080/jcmah.2019.10.555787

Lovely by the Water- Reflections on the Pleasures and Benefits of Doing Qigong in Natural Surroundings

2019· article· en· W4390627501 on OpenAlexaff
Bernie Warren

Bibliographic record

VenueJournal of Complementary Medicine & Alternative Healthcare · 2019
Typearticle
Languageen
FieldMedicine
TopicBiofield Effects and Biophysics
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsNatural (archaeology)Context (archaeology)BuddhismPerceptionHealth benefitsSituatedPsychologyAestheticsMedicineHistoryTraditional medicineArtComputer scienceArtificial intelligenceArchaeology

Abstract

fetched live from OpenAlex

Qigong and Nature are intertwined. The image of water is omnipresent in Taoist philosophy as Taoist sages believe we are always immersed in Tao that our lives are spent in this moving river of life. Images of trees usually willows situated near water their branches moving in the gentle breeze permeate the ancient texts which guide Qigong practice. While there has been considerable research recently on Green and Blue Spaces on exercise in general however, there has been very little modern research on the health or any other benefits of practicing Qigong outside in natural surroundings. This article offers a commentary on the experience and perceived benefits of practicing Qigong in natural surroundings. It presents 20 years of teaching Qigong classes in Natural surroundings through the eyes of my students and of Qigong colleagues around the world and places these in the context of academic research. It concludes that the ancient Taoist and Buddhist master’s perceptions written many years ago are correct. The quality of the experience health benefits, and the energy accrued from Qigong is enhanced by practice in nature.

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0060.014
Scholarly communication0.0030.004
Open science0.0010.004
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0040.001

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 head0.074
GPT teacher head0.369
Teacher spread0.295 · 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 designNot applicable
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
Published2019
Admission routes1
Has abstractyes

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