The effect of shading in extracting structure from space-filling visualizations
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
Shading information is extracted by the human visual system during the earliest stages of the object recognition process. While shading can enhance the visibility of structures, it can have a negative impact on the judgment of sizes of elements in a structure. In certain visualization systems the underlying hierarchical structure is not noticeably explicit, such as in space-filling techniques. We hypothesize that in such cases, shading can make the structure more explicit. In This work we report the results of a study comparing two space-filling visualizations: one with and the other without shading. Our results show that on structure-based tasks, users performed better with the tool when shading information was included than without. However we did not obtain statistically significant results to suggest that shading information degraded users' performance on tasks requiring comparison of local features such as file sizes. A subjective evaluation shows that users preferred interacting with the system when shading was available.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.001 | 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