Superposition-based approach to generating river bathymetry: A case study
Bibliographic record
Abstract
The accuracy of two- or three-dimensional hydraulic modelling of river flow depends significantly on a realistic description of river channel and floodplain geometries in the form of a continuous, seamless topobathymetry digital elevation model (TB-DEM). However, bathymetric information for most rivers is not available in ready-to-use digital data format. The existing methods of generating TB-DEMs require access to raw data and ground measurements to some extent. This study proposed a simple superposition-based approach, comprising geographic information system-based interpolation and geoprocessing techniques to generate a seamless elevation model using topography and bathymetry from secondary data sources, including crowdsourcing. TB-DEM was generated for the St. Lawrence River and Ottawa River upstream of Montreal in Quebec. The upland topography was unaffected by the superpositioning process, whereas the interpolated bathymetry showed significant positive linear associations with the reference elevation data. The vertical accuracy of the bathymetry digital elevation model was 1.43 m in root-mean-squared error.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".