Integrating SHriMP with the IBM websphere studio workbench
Bibliographic record
Abstract
This paper provides an experience report for researchers who are interested in integrating their tools with the new IBM WebSphere Studio Workbench. The Workbench (open source at www.eclipse.org) provides an open framework for building integrated development environments. We report on our experience integrating an information visualization tool (called SHriMP Views) with the IBM Workbench. Although SHriMP was originally developed for visualizing programs, it is content independent. We have re-targeted SHriMP for visualizing flow diagrams. Flow diagrams can be hierarchically composed, thus leveraging the key features of SHriMP that allow a user to easily navigate hierarchically composed information spaces. We discuss the di#erences between programs and flow diagrams both in terms of their semantics and in their visual representation. Terminals, which are a first-class entity that mediate between nodes and arcs in flow diagrams, presented the main challenges here. We also report on the main technical challenges we faced, due to the di#erent widgets sets used by SHriMP (Swing/awt) and the Workbench (swt).
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.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".