Application of spatial cross correlation to detection of migration of submarine sand dunes
Bibliographic record
Abstract
Knowledge of migration rates of bedforms provides an indirect indication of the behavior of tidally averaged bottom currents, enables optimization of hydrographic survey frequency and may enable calculation of bedload transport rate. To measure bedform migration rate, we test the use of spatial correlation as a measurement method, which quantifies and locates a region of maximum similarity between two spatial variables. For the latter, we use consecutive eight‐bit images of spatial gradient, derived from bathymetric digital terrain models, carrying out the correlation over this representation of the shape of the seabed rather than the bathymetric surface. The digital terrain models were compiled from six repeat multibeam surveys of a headland‐associated bank near Saint John, New Brunswick, with a roughly 30‐day interval. Vectors are drawn depicting the movement of a sand dune at time t0 toward a point in the spatial correlation array at a later time, t1. A number of different techniques of picking the end of the migration vector were used. The sinuosity of the dune crest at the scale of the correlation window has an impact on which migration vector is the better pick. Averaging of migration vectors from consecutive epochs diminishes random errors in the correlation picks using any single pair of images and creates a more accurate picture of the migration field. Migration rates and crest‐relative migration directions vary substantially across the sand bank, reflecting the high gradients in bottom shear stress around the headland.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| 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) | 0.001 | 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".