Canada1Water Digital Terrain Model with Bathymetry dataset
Bibliographic record
Abstract
National integrated water modelling of Canada required a (DEM) Digital Elevation Model for all continental Canada at a single resolution, with bathymetry, and inclusion of transboundary watersheds with the United States. Such a model was not available and hence the Canada1Water project developed a Digital Terrain Model (DTM) that integrates high resolution elevation data from derivative Arctic (2 m) and NASADEM (30 M) DEM with open-source bathymetry data for lakes ≥10 km2. The DTM was brought to a standard datum and projection, resampled to a standard horizontal scale, and corrected for void issues and canopy biases. The result is the 1-arcsecond grid (~20 m) Canada1Water Digital Terrain Model (C1W-DTM). The new DTM provides a standard elevation datum for all subsurface modelling as well as a constraint for the surface hydraulic flow system. A seamless DTM with lake Bathymetry was essential to support the control volume HydroGeoSphere modelling environment being used. This dataset supports a wide range of applications requiring a DTM with lake bathymetry thus enabling more accurate and informed decision-making. The final prudct is a 3-band raster GeoTIFF, comprising of the DSM, DEM, DTM with lake bathymetry. Read the full Data in Brief for methodology here: Kessel, E. D., Frey, S. K. & Russell, H. A. J. (2026). Canada1Water Digital Terrain Model with Bathymetry dataset. Data in Brief, 112878. https://doi.org/10.1016/j.dib.2026.112878Visit the Canada1Water Data Portal here: https://portal.canada1water.ca/
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.011 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.047 | 0.026 |
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".