Energy aware colour mapping for visualization
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
We present a design technique for colours that lower the energy consumption of the display device. Our approach relies on a screen space variant energy model. Guided by perceptual principles, we present three variations of our approach for finding low energy, distinguishable, iso-lightness colours. The first is based on a set of discrete user-named (categorical) colours, which are ordered according to energy consumption. The second optimizes for colours in the continuous CIELAB colour space. The third is hybrid, optimizing for colours in select CIELAB colour subspaces that are associated with colour names. We quantitatively compare our colours with a traditional choice of colours, demonstrating that approximately 45 percent of the display energy is saved. The colour sets are applied to 2D visualization of nominal data and volume rendering of 3D scalar fields. A new colour blending method for volume rendering which preserves hues further improves colour distinguishability.
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 | 0.001 |
| 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 | 0.000 |
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 it