The Mind-Gut Connection: A virtual reality education program on the relationship between the digestive system, nervous system, and microbiome.
Bibliographic record
Abstract
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.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.033 | 0.005 |
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".