PREDICTION OF EQUILIBRIUM SUBAQUEOUS DUNE CHARACTERISTICS USING GENETIC ALGORITHMS
Bibliographic record
Abstract
Sand dunes are ubiquitous in natural subaqueous environments and may pose a significant risk in coastal setups for many domains such as the offshore industry or marine renewable energies (Vantorre et al., 2013 ; Barrie and Conway, 2014). Observations of bedform development show that an initially flatbed evolves through different phases: an incipient bedform phase, a growing phase and a stabilizing phase leading to a fully developed dune field. Finite-amplitude dune equilibrium is essentially controlled by the flow depth, the bed shear stress, and the inertia length of the sediment transport in suspension and for bedload at smaller depths (Dore et al., 2023). Existing predictors fail to describe dune dimensions at equilibrium (height and wavelength) (Bradley and Venditti, 2017). Data availability and the poor description of complex morphodynamic phenomena has led to an increasing use of Machine learning in coastal science (Goldstein et al., 2019). In the present work we use Genetic algorithms (GAs) to perform regression analysis on dune laboratory data available in the literature. Our results showed that GAs outperform usual predictors against performance metrics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".