Toward graph layout of large data visualization: algorithms, evaluations and application
Bibliographic record
Abstract
Generating layouts for millions of points on a spatially-restricted platform is a difficult\ntask with a unique set of constraints. These layouts are traditionally generated on a\nserver out of sight of the user. User-oriented applications would benefit from a real-time\nview of layout generation, which can assist user decision making and improve user\nexperience by introducing interactivity. The literature of constraint resolution and mobile\nvisualization is briefly surveyed to achieve an understanding of the state of the art for\nthis problem, and motivate a solution with scenario-based examples. We formally identify\nthe major constraints associated with this specialized layout generation problem and the\nspecial interplay between them. A pipeline-based layout generation method is defined\nalgorithmically, coupled with the implementation of the algorithm(s). The quality of\nthe result is analyzed on a constraint-dependent basis. Applications and future system\nimprovements and extensions are discussed.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".