Stochastic Heterogeneity Mapping around a Mediterranean salt lens
Bibliographic record
Abstract
Abstract. We present the first application of Stochastic Heterogeneity Mapping based on the band-limited von Kármán function to a seismic reflection stack of a Mediterranean water eddy (meddy), a large salt lens of Mediterranean water. This process extracts two stochastic parameters directly from the reflectivity field of the seismic data: the Hurst number, which ranges from 0 to 1, and the correlation length (scale length). Lower Hurst numbers represent a richer range of high wavenumbers and correspond to a broader range of heterogeneity in reflection events. The Hurst number estimate for the top of the meddy (0.39) compares well with recent theoretical work, which required values between 0.25 and 0.5 to model internal wave surfaces in open ocean conditions based on simulating a Garrett-Munk spectrum (GM76) slope of −2. The scale lengths obtained do not fit as well to seismic reflection events as those used in other studies to model internal waves. We suggest two explanations for this discrepancy: (1) due to the fact that the stochastic parameters are derived from the reflectivity field rather than the impedance field the estimated scale lengths may be underestimated, as has been reported; and (2) because the meddy seismic image is a two-dimensional slice of a complex and dynamic three-dimensional object, the derived scale lengths are biased to the direction of flow. Nonetheless, varying stochastic parameters, which correspond to different spectral slopes in the Garrett-Munk spectrum (horizontal wavenumber spectrum), can provide an estimate of different internal wave scales from seismic data alone. We hence introduce Stochastic Heterogeneity Mapping as a novel tool in physical oceanography.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".