Development of Moist Singular Vectors in GRAPES-GEPS and a Preliminary Evaluation
Bibliographic record
Abstract
In this study, moist singular vector (MSV) was developed based on GRAPES-GEPS (Global/Regional Assimilation and Prediction System – Global Ensemble Prediction System), the adjoint model of large-scale condensation and cumulus deep convection in GRAPES-4DVar (Four-dimensional variational assimilation). Five consecutive days of numerical experiments were performed for a preliminary evaluation of MSV. The singular values, horizontal distribution structure, spread of MSVs perturbation and its influence on the ensemble prediction were compared for each group of tests. The results showed that in the middle and high latitudes of the northern and southern hemispheres, the addition of both linearized moist physical processes increased the spread of the mid- and low-level SVs, but the linearized large-scale condensation (LC) process plays a leading role in the structure of MSV. The analysis of ensemble forecast shows the inclusion of moist linearized physical processes led to a greater effect of MSV on the rainfall levels of 10 and 25 mm and a slight improvement in anomaly correlation coefficient (ACC) of the atmospheric circulation field, and more obvious improvement due to linearized large-scale condensation. In the future, continuous multi-year testing and tropical-specific analyses are required for operation.
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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 | 0.001 | 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) | 0.003 | 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".