Wirelength and congestion estimation for routability-driven placement
Bibliographic record
Abstract
Summary form only given. Historically over the years, IBM has supported a wide array of university relations programs, at the corporate, business unit/product division and local (geographical) levels. These programs have been used to support a variety of objectives that include: product innovation, testing, proof-of-concepts and showcases; talent development and recruiting; sales opportunities; corporate citizenship and visibility; and others. An effective application of these university relations resources and programs occurs within the Chief Technology Offices (CTOs) of several IBM Software Group (SWG) product divisions. A notable example is WebSphere CTO, and specifically its Emerging Technology Institute (ETI), which defines the new products, new features, new technologies etc. for the IBM WebSphere product portfolio. It maintains a pair of regional university relations centers, the Centers for Advanced Studies (CAS), located in Research Triangle Park, North Carolina (US) and Toronto (Canada), which are also co-located at two of WebSphere's largest product development sites. These centers work directly with the local universities in support of product innovation and (student) talent development and recruitment, and are part of a larger network of some 26 such centers located around the world. This talk will provide an overview of the various IBM corporate university relations programs that are managed by the Global University Programs (GUP) team. These include the IBM Shared University Relations (SUR) program, the IBM Faculty Awards and Innovation Awards programs, the IBM PhD Fellowship program, the Open Collaborative Research (OCR) program and many others. It will also provide an overview of other special programs within IBM that support educational and research institutions, including the Academic Initiative, the Systems and Technology
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".