Application of the Semi-Lagrangian Inherently Conserving and Efficient (SLICE) Transport Method to Divergent Flows on a C Grid
Bibliographic record
Abstract
Abstract Local conservation with the Semi-Lagrangian Inherently Conserving and Efficient (SLICE) transport method with a new trajectory algorithm is studied. Validation results of 1D and 2D passive advection with this new algorithm, which converges twice as fast as the old one, on the Arakawa C grid of a model in Cartesian coordinates are obtained. The effects of numerically computed divergence and trajectories on the results were also investigated. Random small-scale errors due to the divergence, especially with realistic winds, can be observed. The total mass is conserved, however, and is not affected since the results show clearly that SLICE ensures a perfect local conservation. This work represents the first step toward implementation of SLICE in the operational Canadian Global Environmental Multiscale (GEM) model.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".