Evolutionary Prediction of the Trajectory of a Rosette Momentum Jet Group in Flowing Currents
Bibliographic record
Abstract
Yan, X. and Mohammadian, A., 2020. Evolutionary prediction of the trajectory of a rosette momentum jet group in flowing currents. Journal of Coastal Research, 36(5), 1059–1067. Coconut Creek (Florida), ISSN 0749-0208.This study proposes a new approach to predicting the trajectory of a rosette momentum jet group in flowing currents, using multigene genetic programming (MGGP), which is an evolutionary-based artificial intelligence (AI) technique. The MGGP algorithm is used to develop explicit mathematical models that predict the dimensionless coordinates of the jet centerline trajectory as functions of the jet-to-ambient velocity ratios, the Reynolds numbers, the dimensionless jet angle, and the dimensionless travel distance. Experimental data are used to train the models, and the optimal models are identified using the Pareto-optimal approach, based on a performance–complexity trade-off. The same data, and some additional unseen data, are used to assess the performances of the developed models. The results show that the MGGP predictions have a good match with both the training and testing experimental datasets. The best MGGP model is also found to be superior to the best single-gene genetic programming (SGGP) model. This study demonstrates the suitability and capability of the MGGP technique in developing models for predicting the trajectory of a rosette momentum jet group in flowing currents, which can be used in many applications in the field of coastal science and engineering, such as the design of coastal outfall systems and assessment of environmental impacts.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".