Measuring the Psychological Markers of Birds Encounter Using Virtual Reality Environments
Bibliographic record
Abstract
Interactions between humans and animals benefit human health and well-being. There has been little study on the effects of bird encounters on people. This study addresses this conceptual gap by analyzing the results of a between-subjects experiment with 136 undergraduate students who were randomly assigned to experience one of four 360 stereo panorama virtual environments representing four encounters, namely "bird depicted in images," "Birds in a Cage," "Watching Birds in Nature inside a path," and "Birds in nature." Following their encounter in a virtual environment, participants assessed their experience in terms of spatial awareness, emotions, psychological well-being, and connection to nature through the use of an online survey. Standard descriptive statistics, correlation, Kruskal–Wallis, and post hoc Bonferroni analysis are used in data analysis. The findings suggested that experiences with birds in open surroundings were more likely to affect participants' spatial perception, emotions, psychological health potentials, and connection to nature than encounters with birds in confined places. This study is critical for environmental awareness since maintaining biodiversity and wildlife is inextricably linked to the potential well-being and quality of life of the human population who are part of the ecosystem.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".