Assessing statistical fitting methods used for estimating turbulence parameters
Bibliographic record
Abstract
Abstract Many turbulence estimates require fitting model forms, typically nonlinear expressions, to observations that have been converted into spectra. Choosing a fitting method usually depends on user preference, such as calculation ease under the spectra's presumed statistical nature or reducing computation demands when turbulence quantities must be estimated onboard expendable instruments. Six different methods are assessed by fitting a known model against synthetic spectra with variability generated from two different statistical distributions. The assessment uses an inertial subrange model to estimate the turbulent kinetic energy dissipation rate from velocity spectra. However, the results and conclusions are relevant to fitting other turbulence inertial subrange models that follow a power law where is the spectral slope and contains the sought‐after turbulence parameter. The two most accurate methods require linearizing the spectral observations by taking the logarithm of the wavenumbers and the dependent spectra power density . These methods are less sensitive to outliers and deviations of the observations from a known statistical distribution. Some methods returned that deviated from the prescribed value by more than 50% depending on the number of samples fitted and the level of uncertainty of the spectra. Methods for estimating the spectral slope, , were also assessed to provide recommendations on using this parameter to flag data which deviates from the expected form so that the spectra (or wavenumbers) can be excluded from further analysis.
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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 | 0.107 | 0.309 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".