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Record W3014112452 · doi:10.11575/prism/37410

The Mind-Gut Connection: A virtual reality education program on the relationship between the digestive system, nervous system, and microbiome.

2019· article· en· W3014112452 on OpenAlexvenueno aff
Ryan M. Lee, Christian Jacob, Keith A. Sharkey

Bibliographic record

VenueLibraries and Cultural Resources (University of Calgary) · 2019
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience, Education and Cognitive Function
Canadian institutionsnot available
Fundersnot available
KeywordsMicrobiomeGut microbiomeConnection (principal bundle)Enteric nervous systemBiologyPsychologyCognitive scienceNeuroscienceBioinformaticsEngineering

Abstract

fetched live from OpenAlex

The Mind-Gut Connection is a virtual reality education application on the relationship between the digestive system, nervous system, and microbiome. Together, these systems form the gut-brain axis and communicate with one another to carry out physiological processes associated with digestion. By illustrating this complex medical topic in a virtual reality environment, we have addressed the lack of accurate or comprehensive depictions of the gut-brain axis. Additionally, the use of virtual reality in education may allow for a broader audience to be exposed to this information. Learning about digestion in relation to the gut-brain axis is beneficial for everyone because of the impact our diets and lifestyles have on our physical and mental health. The use of this virtual reality program has the potential to better engage and inform the general public so that they are more aware of how our different body systems are interconnected. Not only is this program novel in addressing such a unique but important topic, it also exhibits innovation upon current virtual reality practices surrounding movement and motion sickness. The use of full-body virtual reality and a natural form of locomotion using arm swinging builds upon existing methods to improve the level of immersion and believability.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.832
Threshold uncertainty score0.999

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 studies0.0020.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.030
GPT teacher head0.230
Teacher spread0.200 · 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 designTheoretical or conceptual
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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