An overview of the Los Alamos Crestone Project : uses for astrophysical problems
Bibliographic record
Abstract
The Los Alamos Crestone Project is part of the Department of Energy's (DoE) Advanced Simulation and Computing (ASC) program. The main goal of this project is to investigate the use of continuous adaptive mesh refinement (CAMR) techniques for application to problems of interest to the Laboratory. An overview of the astrophysical simulations performed with the SAGE/RAGE codes will be shown here, including asteroid impacts in the deep-ocean, asteroid impacts on the continental shelf (e.g. - Chicxulub - the dinosaur killer), calculations of massive black holes at the galactic center, and calculations of supernova explosions. Examples of these simulations will be shown. We have shown that the SAGE and RAGE codes of the Crestone Project have been very successful products of the DoE's Advanced Simulation and Computing program. It is clear to those performing massively-parallel computations, that the use of thousands of processors in parallel is fundamentally changing the way we think about computer simulations. The Crestone Project codes are fully utilizing each new ASC supercomputer as they become available. The SAGE and RAGE codes are sophisticated Continuous Adaptive Mesh Refinement hydrodynamics codes for large parallel simulations. SAGE and RAGE are becoming useful tools for astrophysical applications. Further research is starting in a wider variety of areas, including cosmological studies with Mike Norman's group at UCSD.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 0.013 |
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".