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
Interacting with nature is beneficial to a person's mental-state, but it can sometimes be difficult to find environments that will induce positive affect (e.g., when planning a run). In this paper, we describe EnviroPulse-a system for auto-matically determining and communicating the expected affective valence (EAV) of environments to individuals. We describe a prototype that allows this to be used in real-time on a smartphone, but EnviroPulse could easily be incorporated into GPS systems, mapping services, or image-based systems. Our work differs from existing work in af-fective computing in that, rather than detecting a user's affect directly, we automatically determine the EAV of the environment through visual analysis. We present results that suggest our system can determine the EAV of envi-ronments. We also introduce real-time affective visual feedback of the calculated EAV of the images, and present results from an informal study suggesting that real-time visual feedback can be used for induction of affect.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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