The Wind-Current-Water Levels Effect over Surface Wave Parameters Nearby the Magdalena River Delta: A Numerical Approach
Bibliographic record
Abstract
The river deltas are hydrodynamic systems where high energy flux occurs due to the interaction of the river discharge, winds, water levels and wave field.This interaction triggers complex surface non-linear interactions that affect the wave parameters at the river delta, hence, non-linear analysis methods might ease the understanding of intricate surface ocean processes.Then, this research selected the Magdalena River delta to perform a novel application of a DOE-ANOVA.2 3 factorial design using winds, surface currents, and water levels as factors and surface wave parameters such as significant wave heights and peak period as responses.The factor's data was retrieved from calibrated and validated hydrodynamic modelling of the main climate seasons (February, June, and October, respectively) in 2010, which is the year reporting the lowest and highest water levels in the river before 2015.The DOE-ANOVA results evidenced that winds modulated the surface wave parameters suggesting quadruplets wave-wave interactions, white-capping dissipation, and a surface river plume curvature due to the wind effect.The water level and currents at the river delta controlled the wave parameters, modulating the wave energy distribution between kinetic and potential.Finally, this research expanded the use of the DOE-ANOVA factorial design through factors and responses handled in time series, what eased to analyze the cause and effect within complex ocean surface interactions among wind, currents, water levels and waves.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".