Advanced 3D Mapping of Hydrodynamic Parameters for the Analysis of Complex Flow Motions in a Submerged Bedrock Canyon of the Tocantins River, Brazil
Bibliographic record
Abstract
Validation and scaling of sophisticated physical and numerical fluvial hydraulic models to real field conditions are limited by temporal and spatial constraints of field measurement technologies. These limitations increase when analyzing hydraulic properties of complex river forms such as submerged bedrock canyons. The analysis of flow under these conditions has demonstrated non-linear behavior, strong secondary circulation and a high level of turbulence. The objective of this paper is to obtain and analyze turbulence and secondary current information for flow analysis, by evaluating the spatial distribution of bed shear stress and eddy viscosity with acoustic Doppler current profiler (ADCP) measurements. The real field-scale case study was the Lourenço Rock Canyon in Tocantins River, Brazil. A total of 10,531 velocity profiles were measured with an ADCP adapted to a moving boat. The data were post-processed by decomposing the velocity data measurements and calculating the standard deviations. Three different methods are compared for bed shear stress: total kinetic energy (TKE), law of the wall and the depth–slope product. The eddy viscosity was estimated with the Boussinesq approach. The initial data processing confirmed turbulence and flow velocity characteristics described in similar literature findings. The results for the bed shear stress for the canyon area were 8 times greater than the outside and for eddy viscosity, 10 times higher. For the surroundings area, the eddy viscosity showed reasonable results with values around the regular 1.0 m2/s used for rivers. It is concluded that turbulent values can be calculated to better represent physical processes with the intention to improve hydrodynamic numerical models calibrations.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".