High-Performance Modelling for the Mesoscale Alpine Programme (MAP) Field Experiment
Bibliographic record
Abstract
With the development of a version of the Canadian non-hydrostatic MC2 model optimized for massively parallel processors, it has become possible to solve very large weather forecast problems in a time sufficiently short to envision a realtime daily calculation over a domain covering the entire Alps. MAP is a large multinational research program that will gather new knowledge about the heavy precipitation over steep topography; its field phase is to take place during Fall of 1999. The tentative grid dimensions are 490 − 400 − 35 at a horizontal resolution of 2 km, with possibly a need to increase the number of vertical layers. This is a very large problem to be solved with realtime constraints. The model results will be accessible to the forecasters at the MAP Operations Centre in Innsbruck, to assist in the scientific briefings to dispatch the flight plans of the research aircraft. The forecasts are planned to be performed on the NEC-SX/4 (10 PE) at the CSCS (Centro Svizzero di Calcolo Scientifico) in Manno (Canton Ticino, Switzerland), which is affiliated with the ETH. The current state of this joint effort is presented.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".