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
Since graphs are ubiquitous representations of data that are used in many applications, creating graph layouts is an important problem. These graph layouts are usefully discussed in terms of aesthetics that originated from mathematical concepts. In contrast, we explore the use of alternative aesthetics to inspire the visualization of graphs. We present Daisy Visualization, for which we have designed a new graph layout that is inspired by ornamental patterns of daisy flowers. In Daisy Visualization, graphs' attributes are mapped to floral elements to create an attractive information visualization that might more readily hold viewers' attention. As a practical use case we apply Daisy Visualization to the layout of ecological networks based on real ecosystem datasets. We show how specific attributes of ecological networks such as input/output edges, or respiration, can be mapped to floral elements. We conducted a qualitative assessment of Daisy Visualization, where we obtained overall positive feedback and interesting specific thoughts about various design decisions and possible future directions.
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 | 0.000 |
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