Workshop and conference on Grand Challenges applications and software technology
Bibliographic record
Abstract
On May 4--7, 1993, nine federal agencies sponsored a four-day meeting on Grand Challenge applications and software technology. The objective was to bring High-Performance Computing and Communications (HPCC) Grand Challenge applications research groups supported under the federal HPCC program together with HPCC software technologists to: discuss multidisciplinary computational science research issues and approaches, identify major technology challenges facing users and providers, and refine software technology requirements for Grand Challenge applications research. The first day and a half focused on applications. Presentations were given by speakers from universities, national laboratories, and government agencies actively involved in Grand Challenge research. Five areas of research were covered: environmental and earth sciences; computational physics; computational biology, chemistry, and materials sciences; computational fluid and plasma dynamics; and applications of artificial intelligence. The next day and a half was spent in working groups in which the applications researchers were joined by software technologists. Nine breakout sessions took place: I/0, Data, and File Systems; Parallel Programming Paradigms; Performance Characterization and Evaluation of Massively Parallel Processing Applications; Program Development Tools; Building Multidisciplinary Applications; Algorithm and Libraries I; Algorithms and Libraries II; Graphics and Visualization; and National HPCC Infrastructure.
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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.122 | 0.053 |
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".