Bayesian Belief Network Analysis of a Large Eddy Simulated Ocean Turbulence Field
Bibliographic record
Abstract
An observational space Bayesian belief network analytical formulism is applied to a sub-grid modeled turbulent kinetic energy (tke) field emanating from ocean turbulence large eddy simulation (LES) data containing Langmuir cells but no breaking waves.The purpose of the analysis is to illustrate how statistical machine learning modeling can be used to understand the probabilistic structure of observational space which is needed in demonstrating how the allied latent space can be related statistically to it for the purpose of data generation.The Peter and Clark (PC)-algorithm-based Bayesian belief network (BBN) edge-nodal structure for the observational-space tke subdomains demonstrates a distinctive nonlocal connectivity pattern when the multidimensional scaling graph layout is invoked.When the Chow-Liu algorithm is used, a different tree-based connectivity in the network is revealed.In particular, a dominant parental root node occupies the far upper left region in the observational-space domain with many edge connections flowing toward the right.Quantification of linkage strength through the BBNs provides a way to understand the spatial covariance of observational space subdomains providing hints as to the physically relevant statistical inferential model.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".