Numerical Prediction of the Drying-WettingProcess in a River Leveeand Floodplain
Bibliographic record
Abstract
One third of the economic losses due to natural disasters in the last century has been caused by floods.Today the flood hazard is increasing due to more frequent extreme hydrological events, caused by climate change, and to the land use change and urbanization, that reduce the water storage capacity of the subsoil and the floodplain areas.An appropriate planning of river levees and floodplains is required to protect inhabited areas and infrastructures.At the same time, an efficient management, assisted by effective monitoring and prediction tools, ensures the performance of the defence systems.In this context, a land reprofiling was designed for river Secchia in Northern Italy, to widen the natural floodplain.In the paper the response of the new system to flood events is assessed by finite element analyses able to describe the soil-water-atmosphere interaction.The reliability of the numerical prediction relies on a proper description of both the hydraulic behaviour of the unsaturated soil, i.e. the water retention curve and the permeability function, and the external load history, established from the hydraulic and atmospheric boundary conditions.Indirect methods for the estimation of the Soil Water Retention Curve may involve significant limits if not combined with a laboratory information on the soil nature and state.Moreover, the numerical results show that the drying-wetting process is highly dependent on the initial saturation conditions of the subsoil, thus making it essential to simulate a sufficiently long load history.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".