Developments in teracomputing : proceedings of the Ninth ECMWF Workshop on the Use of High Performance Computing in Meteorology : Reading, UK, November 13-17, 2000
Bibliographic record
Abstract
Research and development of the earth simulator, K. Yoshida and S. Shingu parallel computing at Canadian Meteorological Centre, J-P Toviessi et al parallel elliptic solvers for the implicit global variable-resolution grid-point GEM model - iterative and fast direct methods, A. Qaddouri and J Cote IFS developments, D. Dent et al performance of parallelized forecast and analysis models at JMA, Y. Oikawa building a scalable parallel architecture for spectal GCMS, T.N. Venkatesh et al semi-implicit spectral element methods for atmospheric general circulation models, R.D. Loft and S.J. Thomas experiments with NCEP's spectral model, J-F Estrade et al the implementation of I/O servers in NCEP's ETA model on the IBM SP, J. Tuccillo implementation of a complete weather forecasting suite on PARAM 10 000, S.C. Purohit et al parallel load balance system of regional multiple scale advanced prediction system, J. Zhiyan grid computing for meteorology, G-R Hoffmann the requirements for an active archive at the Met Office, M. Carter intelligent support for high I/O requirements of leading edge scientific codes on high-end computing systems - the ESTEDI project, K. Kleese and P. Baumann coupled marine ecosystem modelling on high-performance computers, M. Ashworth et al OpenMP in the physics portion of the Met Office model, R.W. Ford and P.M. Burton converting the halo-update subroutine in the Met Office unified model to co-array Fortran, P.M. Burton et al parallel ice dynamics in an operational Baltic Sea model, T. Wilhelmsson parallel coupling of regional atmosphere and ocean models, S. Frickenhaus et al dynamic load balancing for atmospheric models, G. Karagiorgos et al HPC in Switzerland - new developments in numerical weather prediction, M. Ballabio et al the role of advanced computing in future weather prediction, A.E. MacDonald the scalable modelling system - a high-level alternative to MPI, M. Govett et al development of a next-generation regional weather research and forecast model, J. Michalakes et al parallel numerical kernels for climate models, V. Balaji using accurate arithmetics to improve numerical reproducibility and stability in parallel applications, Y. He and C.H.Q. Ding parallelization of a GCM using a hybrid approach on the IBM SP2, S. Cocke and Z. Christidis developments in high performance computing at Fleet Numerical Meteorology and Oceanography Center, K.D. Pollak and R.M. Clancy the computational performance of the NCEP seasonal forecast model on Fujitsu VPP5000 at ECMWF, H-M H. Juang and M. Kanamitsu panel experience on using high performance computing in meteorology - summary of the discussion, P. Prior.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.054 | 0.030 |
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".