Large Atmospheric Computation on the Earth Simulator: The LACES Project
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it