EVolve
Bibliographic record
Abstract
Existing visualization tools typically do not allow easy extension by new visualization techniques, and are often coupled with inflexible data input mechanisms. This paper presents EVolve, a flexible and extensible framework for visualizing program characteristics and behaviour. The framework is flexible in the sense that it can visualize many kinds of data, and it is extensible in the sense that it is quite straightforward to add new kinds of visualizations.The overall architecture of the framework consists of the core EVolve platform that communicates with data sources via a well defined data protocol and which communicates with visualization methods via a visualization protocol.Given a data source, an end-user can use EVolve as a stand-alone tool by interactively creating, configuring and modifying visualizations. A variety of visualizations are provided in the current EVolve library, with features that facilitate the comparison of multiple views on the same execution data. We demonstrate EVolve in the context of visualizing execution behaviour of Java programs.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".