Sensitivity analysis of a morphodynamic modeling system applied to a Portuguese tidal inlet
Bibliographic record
Abstract
Coastal area morphodynamic models are prone to severe errors arising from various sources, including: (1) the use of empirical sediment transport formulae (Pinto et al., 2006); (2) the reliability of the data used to feed the models; (3) the use of simplifying physical assumptions; (4) the error propagation between the various modules.Since these errors can be significant, the usefulness and credibility of morphodynamic simulations require a thorough understanding of their uncertainty.This paper addresses the practical implications of these input parameters on the development of a tidal inlet.A sensitivity analysis is performed through the application of the morphodynamic modeling system MORSYS2D (Fortunato and Oliveira, 2004) to the Óbidos lagoon (Oliveira et al., 2005), a small but very rapidly evolving coastal system located in western Portugal.The influences of: (1) sediment characteristics; (2) the choice of the forcing tide and (3) the sediment transport formula are analysed, namely through the inlet cross-section evolution and the ebb-delta development.The choice of the forcing tide appears important, since the use of a real tide, rather than a representative tide, induces: (1) faster morphological changes; (2) 15-day cyclic evolutions (spring-neap tidal cycle); (3) larger ebb-deltas and inlet cross-sections.Sediment grain size and empirical transport formulae rather affect the rapidity of morphological changes, since equilibrium is reached after 3 months of simulations and final inlet morphologies are noticeably comparable.Nevertheless, the good agreement of the results after three months of simulation demonstrates the reliability of the modelling system on the time scales of months.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".