The conditional mean acceleration of fluid particle in developed turbulence
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Bibliographic record
Abstract
Using the random intensity of noise (RIN) approach to the one-dimensional Laval-Dubrulle-Nazarenko type model for the Lagrangian acceleration in developed turbulence [cond-mat/0305186, cond-mat/0305459] we study the probability density function and mean acceleration conditional on velocity fluctuations. The additive noise intensity and the cross correlation between the additive and multiplicative noises are assumed to be dependent on velocity fluctuations in an exponential way. The obtained fit results are found to be in a good qualitative agreement with the recent experimental data on the conditional acceleration statistics by Mordant, Crawford, and Bodenschatz. The fit to the observed conditional mean acceleration is of pure illustrative character which is performed to study influence of variation of the cross correlation parameter on the shape of conditional acceleration distribution and conditional acceleration variance. The conditional mean acceleration should be zero for homogeneous isotropic turbulence. The observed conditional mean acceleration increases for bigger velocity fluctuation amplitude and is associated to anisotropy of the studied flow.
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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)
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Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
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