A two dimensional hydrodynamic river morphology and gravel transport model
Bibliographic record
Abstract
A depth-averaged two-dimensional hydrodynamic-morphological and gravel transport model is presented. Based on River2D by Prof Peter Steffler et al. (2002) of the University of Alberta, River2D-Morphology (R2DM) is capable of simulating flow hydraulics, sediment transport for uni-size and mixed size sediment, and morphological changes of a river over time. Changes in bed elevation are calculated by solving Exner’s equation for sediment mass conservation with the mixed size sediment transport function of Wilcock and Crowe (2003). R2DM is capable of exploring the dynamics of grain size distributions, including fractions of sand in sediment deposits and on the bed surface of rivers and streams. Results have been verified with experimental data for aggradation and downstream fining (Paolo, 1992; Seal, 1992; Toro-Escobar, 2000); for degradation (Ashida and Michiue, 1971); and qualitatively with two dimensional flow (Tsujimoto, 1987). Application of R2DM to actual rivers has shown that it is ready for general release as a tool to assist engineers with river restoration projects, the design of hydraulic structures and with fish habitat modeling.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".