Bibliographic record
Abstract
BACKGROUND BeeScapes is an immersive 360° / VR nature documentary-artwork, written, directed and produced by the lead researcher. Others on the research team served as scientific and creative consultants. Research shows that VR experiences that allow audiences to embody animals increases care for and involvement with environmental issues (Ahn et al, 2016). Until this project, there had been little, if any, examples of scientifically-accurate 360° video or VR experiences that allow audiences to experience aspects of the senses of bees. While research team members had developed methods for representing bee colour vision as well as bee spatial perception, these findings had not previously been represented in an immersive and interactive form to engage and excite general audiences about these topics. The research asks: Can a 360° / VR artwork-documentary be a suitable method for engaging general audiences about bee science? CONTRIBUTION BeeScapes is an interactive, animated 360° / Virtual Reality (VR) experience, exploring environmentalism, non-human realities, and systems of nature that are ordinarily invisible to humans. As an interdisciplinary team, we collaborated to explore how scientific principles could best be translated into an immersive artwork. Customised effects were programmed to depict various aspects of sensory perception. The work involved an iterative process of prototyping, testing, reflection on action, and refining. SIGNIFICANCE It is a rare example of a scientifically accurate VR-artwork that engages audiences to experience the senses of a bee. It has been viewed in excess of 130K times, including from international audiences. It was awarded $20K from the City of Melbourne and Creative Victoria. A virtual exhibition was held on an online website (beescapesfilm.com), supported by both funding bodies. It was showcased in the FIVARS Festival with physical exhibitions in Los Angeles and Toronto.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.264 | 0.096 |
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".