Four-dimensional generalization of ensemble singular vector: Formulation and experiments with the Lorenz 96 model
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Bibliographic record
Abstract
Abstract Motivated by the need to extend sensitivity analysis beyond spatial variations to include temporal evolution, we propose a four‐dimensional generalization to the ensemble singular vector approach, termed 4DEnSV. This generalization enables user‐defined norms that flexibly target spatiotemporal evolutions of interest. Experiments with the Lorenz '96 model demonstrate that 4DEnSV successfully identifies perturbations yielding the largest response under a user‐defined norm. By defining norms to reflect temporal objectives, 4DEnSV can extract initial perturbations responsible for specific temporal changes, such as shifts in peak timing. The proposed method offers a novel framework for sensitivity analysis and related applications, particularly for understanding the temporal evolution of weather systems.
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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 it