Dataset on the dynamics of shallow wakes in gravel-bed floodplains: results of the field experiments on the Tagliamento River, Italy
Bibliographic record
Abstract
Shallow wakes in fluvial environments are hydrodynamic structures produced by in-stream obstructions such as boulders, log jams, patches of riparian and aquatic vegetation. Dynamics of wakes affect a range of biotic and abiotic processes including dispersal of aquatic organisms, mixing of pollutants, sediment deposition and therefore geomorphological changes in rivers. Despite their importance, fundamental mechanisms of shallow wakes development are not fully understood due to lack of experimental data collected in natural environments and free of scaling effects compared to laboratory studies. Present dataset provides explicit information on hydrodynamics of shallow wake flows produced by solid and porous instream obstructions. Controlled field experiments with 30 runs were conducted at the side-branch of the gravel-bed Tagliamento River in the North-Eastern Italy in the period from July till October 2019. During the experimental campaign we varied characteristics of the instream obstructions (their diameter, solid volume fraction, submergence and porosity at the leading edge) and approach velocity. Each run included: (1) measurements of mean velocity and turbulence in the longitudinal (25-30 sampling locations) and lateral transects (8-10 transects with 14 sampling locations) of the constructed experimental channel where model obstructions were placed; (2) detailed surveys of the free surface topography; and (3) flow visualizations and recordings of the wakes patterns using a drone. The dataset is intended to be used for understanding fundamental mechanisms of shallow wakes development in natural fluvial environments, examination of scaling effects and verification of numerical models.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".