Plugging-in visualization
Bibliographic record
Abstract
The Eclipse platform presents an opportunity to openly collaborate and share visualization tools amongst the research community and with developers. In this paper, we present our own experiences of "plugging-in" our visualization tool, SHriMP Views, into this environment. The Eclipse platform's Java Development Tools (JDT) and CVS plug-ins provide us with invaluable information on software artifacts relieving us from the burden of creating this functionality from scratch. This allows us to focus our efforts on the quality of our visualizations and, as our tool is now part of a full-featured Java IDE, gives us greater opportunities to evaluate our visualizations. The integration process required us to re-think some of our tool's architecture, strengthening its ability to be plugged into other environments. We step through a real-life scenario, using our newly integrated tool to aid us in merging of two branches of source code. Finally we detail some of the issues we have encountered in this integration and provide recommendations for other developers of visualization tools considering integration with the Eclipse platform.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.006 |
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".