The architecture of scientific software : IFIP TC2/WG2.5 Working Conference on the Architecuture of Scientific Software, October 2-4, 2000, Ottawa, Canada
Bibliographic record
Abstract
Preface. Contributing Authors. Part I: Large-Scale Systems Integration. Network-Based Scientific Computing E.N. Houstis, et al. Future Generations of Problem-Solving Environments J.C. Cunha. Developing an Architecture to Support the Implementation and Development of Scientific Computing Applications D.C. Arnold, J.J. Dongarra. PETSc and Overture: Lessons Learned Developing an Interface between Components K.R. Buschelman, et al. Component Technology for High-Performance Scientific Simulation Software T. Epperly, et al. A New Approach to Software Integration Frameworks for Multi-physics Simulation Codes E. de Sturler, et al. Code Coupling using Parallel CORBA Objects C. Rene, et al. A Collaborative Code Development Environment for Computational Electro-magnetics M. Shields, et al. Part II: The Architecture of Components. On the Role of Mathematical Abstractions for Scientific Computing K. Ahlander, et al. Object-oriented Modeling of Parallel PDE Solvers M. Thune, et al. Broadway: A Software Architecture for Scientific Computing S.Z. Guyer, C. Lin. Formal Methods for High-Performance Linear Algebra Libraries J.A. Gunnels, et al. New Generalized Matrix Data Structures Lead to a Variety of High-Performance Algorithms F.G. Gustavson. A Comprehensive DFT API for Scientific Computing P.T.P. Tang. Using a Fortran Interface to POSIX Threads R.J. Hanson, et al. Data Management Systems for Scientific Applications R.W. Moore. Software Components for Application Development A. Desitter, et al. Hierarchichal Representation and Computation of Approximate Solutions in Scientific Simulations W.H. Enright. Software Architecture for the Investigation of Controllable Models with Complex Data Sets D. Belyshev, V. Gurman. A Mixed-Language Programming Methodology for High Performance Java Computing V.S. Getov. Part III: Conference Information. The Architecture of Scientific Software: the Conference. Index.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.000 |
| Open science | 0.012 | 0.001 |
| Research integrity | 0.001 | 0.005 |
| 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".