Bayesian Data Characterization and State Prediction for a Large EddyTurbulent Flow Simulation: A Revisitation
Bibliographic record
Abstract
Environmental engineering remote sensing platforms using hyperspectral imagery and other multidimensional modalities are often responsible for monitoring coastal regions in order to safeguard national waters. This objective requires determining sub-surface turbulent structure from surface water flow spatial measurements for state assessment and decisionmaking. The inability of remote sensing platforms to penetrate the water column at depth because of turbulence-induced sediment-concentration modulation necessitates using models that dynamically link surface and sub-surface flow structure. Large eddy simulations (LES) are a useful proxy for the analysis of hyperspectral imagery due to the tri-dimensional structure of both information carrying modalities. Bayesian statistical models are used to revisit the analysis of a large-eddy simulated three-dimensional turbulent shear flow The purpose is the exploration of the feasibility of creating data characterization and state prediction system models for sub-surface vorticity and stress, and surface root mean square (rms) velocity and rms sediment concentration which could then be utilized in the analysis of environmental hyperspectral imagery.
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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".