Machine Learning Based Statistical Characterization of a Turbulence Dissipation Rate Array: A Revisitation
Bibliographic record
Abstract
Comprehension of energy dissipation rates in coastal waters is crucial to understanding such environmental fluid dynamical processes as coastal sediment transport, pollution dispersal, and heat and mass exchange across the air-sea interface. Traditional methods for understanding sub-surface turbulent velocity energetics have focused on the sampling of the turbulent velocity field using acoustic Doppler velocimeters (ADVs) and analyses directed towards the quantification of velocity array variability modeled as statistically independent frequency modes experiencing weak spectral energy transfer. These statistical methods while enlightening have not provided comprehensive insight into spatial ADV array structural dynamics especially in the area of probe system characterization. Understanding the structure of an ADV array as an information system is addressed and accomplished here via the analytical revisitation of three-dimensional velocity data obtained from a four-probe array deployed during the 2001-2003 Coupled Boundary Layers and Air-Sea Transfer (CBLAST) Low Program. The research objective was to show how machine learning algorithms can provide an alternative perspective for the characterization of coastal turbulent velocity information with respect to three important areasmultivariate turbulent kinetic energy level segmentation, nonlinear turbulent velocity modal analysis, and statistical modelling of probe relationships.
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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) | 0.002 | 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".