Characterization of thermohaline staircases in the Tyrrhenian sea using stochastic heterogeneity mapping
Bibliographic record
Abstract
We apply Stochastic Heterogeneity Mapping based on the band-limited von Kármán power law function to stacked migrated seismic data of thermohaline staircases in the Tyrrhenian Sea. This process allows the estimation of stochastic parameters such as the Hurst number (a measure of surface roughness) and scale length. Thermohaline staircases are regular, well-defined step-like variations in vertical profiles of temperature and salinity. In the ocean, they are thought to arise from double diffusion processes driven by the large difference in the molecular diffusivities of heat and salt. They are thought to have an anomalously weak internal wave-induced turbulence, making them suitable for the estimation of a lower detection limit of turbulent dissipation. The Tyrrhenian Sea is a natural laboratory for the study of such staircases because, due to the internal basin’s dynamic stability, steps as small as 10’s of meters can be seen. Lower Hurst numbers represent a richer range of higher wavenumbers corresponding to a broader range of heterogeneity in reflection events. We interpret a broader range of heterogeneity as indicative of a greater degree of turbulence.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".