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.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.065 | 0.018 |
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".