Synthetic spectra for assessing statistical fitting methods used to estimate ocean turbulence
Bibliographic record
Abstract
Many turbulence estimates require fitting expected model forms, typically nonlinear equations, to observations that have been mathematically converted into spectral observations. This database contains synthetic spectral observations used to evaluate several fitting methods, along with the estimated parameters. The impact of degrees of freedom (spectral averaging) was assessed using two datasets with variability generated using two different statistical distributions. The repository contains the following datasets: SyntheticSpectra.nc that contains: 3200 synthetic spectra with the variability drawn from lognormal distribution 3200 synthetic spectra with the variability drawn from chi2 distribution LorNormal.nc that contains fitting results for lognormal dataset Fitting 10 samples in each spectrum Fitting 100 samples in each spectrum Chi.nc that contains fitting results for chi_d^2 dataset Fitting 10 samples in each spectrum Fitting 100 samples in each spectrum The parameters estimated include the sought turbulence (epsilon) quantity and the spectral slope (beta_1). The testing was done as part of ATOMIX SCOR working group #160, with support from NSF grant #OCE-2140395 and contributions from national SCOR committees. The ATOMIX wiki has more information about the group's activities. A manuscript has been submitted relating to the above testing, and this repo will be updated once the citation is available.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.032 |
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".