Can Interactive Map-Based Visualizations Reveal Contexts of Scientific Datasets?
Bibliographic record
Abstract
Existing map-based visualizations of scientific datasets support a small number of tasks. The key reason is that visualizations do not show all properties present in datasets. Due to visualizing only locations in space and time, such visualizations have limited capabilities for visual analytics about contexts of scientific datasets. Visualizing other properties may enhance visual analytics of scientific contexts. The proposed approach is illustrated with a visualization prototype.Les techniques de visualisation actuelle par carte des ensembles de données scientifiques permettent un petit nombre de tâches. La principale raison est que la visualisation ne représente pas toutes les propriétés des ensembles de données. En visualisant uniquement des points à un temps et à un moment précis, une telle technique de visualisation a des capacités limitées aux fins d’analyse visuelle des contextes des ensembles de données scientifiques. La visualisation des autres propriétés peut améliorer l’analyse visuelle des contextes scientifiques. L’approche proposée est illustrée avec un prototype de visualisation.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".