Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Research Background: The Thirst project utilises VR technology, stylised 3D animated graphics and an infrasonic score to draw attention to subterranean activity. The viewer follows the movement of tree roots in pursuit of the precious resource water. Threats from mining, agriculture and drought to Australia’s Great Artesian Basin, the largest body of underground fresh water beneath 23% of the continent, have inspired the development of the project. It is hoped that the embodied sensory experiences that immersion in such VR experiences afford will contribute to a broader cultivation of environmental sensitivity and ultimately wise management of precious natural resources such as water. Research Contribution: Thirst furthers the application of VR as an empathy machine, utilizing embodied sensorial experiences in the service of environmental awareness. At the nexus of science, the sonic and animation arts, Thirst explores and extends the possibilities of cross-disciplinary creative collaboration in the VR space. \nResearch Significance: The project explores a creative relationship between science and the arts, in which science provides insight into environmental issues, and art applies an expressive ‘brush’ to such themes in an effort to engage via the senses, to generate empathy, and to activate social change. \nThe project has been presented at the 30th Society for Animation Studies conference, Concordia University, Montreal, June 19-21, 2018 and the Ecoacoustics Congress, June 2018, and included in the Jalan Jalan On the Move exhibition for Georgetown Festival, Penang, 2018.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.062 | 0.017 |
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 it