Dynamics of shallow wakes on gravel-bed floodplains: dataset from field experiments
Bibliographic record
Abstract
Abstract. Natural dynamics of river floodplains are driven by the interaction of flow and patchy riparian vegetation, which has implications for channel morphology and diversity of riparian habitats. Fundamental mechanisms affecting the dynamics of flow in such systems are still not fully understood due to a lack of experimental data collected in natural environments that are free of the unavoidable scaling effects in laboratory studies. Here we present a detailed dataset on the hydrodynamics of shallow wake flows that develop behind solid and porous obstructions. The dataset was collected during a field experimental campaign carried out in a side branch of the gravel-bed Tagliamento River in northeast Italy. The dataset consists of 30 experimental runs in which we varied the diameter of the surface-mounted obstruction, its solid volume fraction, its porosity at the leading edge, the object's submergence and the approach velocity. Each run included: (1) measurements of mean velocity and turbulence in the longitudinal transect through the centreline of the flow with up to 25–30 sampling locations and from 8 to 10 lateral profiles measured at 14 locations, (2) detailed surveys of the free surface topography and (3) flow visualizations and video recordings of the wake patterns using a drone. The field scale of the experimental setup, precise control of the approach velocity, configuration of models and natural gravel-bed context for this experiment makes this dataset unique. Besides enabling the examination of scaling effects, these data also allow the verification of numerical models and provide insight into the effects of driftwood accumulations on the dynamics of wakes. Data are available with open access via the Zenodo portal (Shumilova et al., 2020) with DOI https://doi.org/10.5281/zenodo.3968748.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".