Large Atmospheric Computation on the Earth Simulator: The LACES Project
Bibliographic record
Abstract
The Large Atmospheric Computation on the Earth Simulator (LACES) project is a joint initiative between Canadian and Japanese meteorological services and academic institutions that focuses on the high resolution simulation of Hurricane Earl (1998). The unique aspect of this effort is the extent of the computational domain, which covers all of North America and Europe with a grid spacing of 1 km. The Canadian Mesoscale Compressible Community (MC2) model is shown to parallelize effectively on the Japanese Earth Simulator (ES) supercomputer; however, even using the extensive computing resources of the ES Center (ESC), the full simulation for the majority of Hurricane Earl′s lifecycle takes over eight days to perform and produces over 5.2 TB of raw data. Preliminary diagnostics show that the results of the LACES simulation for the tropical stage of Hurricane Earl′s lifecycle compare well with available observations for the storm. Further studies involving advanced diagnostics have commenced, taking advantage of the uniquely large spatial extent of the high resolution LACES simulation to investigate multiscale interactions in the hurricane and its environment. It is hoped that these studies will enhance our understanding of processes occurring within the hurricane and between the hurricane and its planetary‐scale environment.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".