Descriptor: Medium Resolution Digital Elevation Model From Natural Resources Canada’s CanElevation Series (MRDEM-30)
Bibliographic record
Abstract
Elevation data are a core theme provided by Natural Resources Canada (NRCan) to Canadians as essential geographic information. Elevation data are a fundamental input for many types of studies and applications and also serve as a basemap for national maps and tile sets. The previous national medium resolution terrain and surface data have not been updated by NRCan since 2011. High-resolution digital elevation models (HRDEMs) are actively being produced by NRCan, but this initiative will take several more years before achieving complete coverage across all of Canada’s land mass. To create a new, modern Canada-wide medium resolution digital elevation model (MRDEM), this work has combined satellite derived Copernicus GLO30 and the HRDEM derived from Light Detection and Ranging (lidar) to produce three datasets: surface model, terrain model and elevation source. The surface and terrain models contain lidar-derived elevation data where available (HRDSM and HRDTM) and GLO30 elsewhere, while the source model raster provides an index, identifying the underlying elevation data source. To generate the terrain model, the GLO30 surface model data was combined with datasets of urban features and forest heights and a variety of geospatial operations were applied to remove features and structures above the ground, then it was fused with HRDTM.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.063 | 0.041 |
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".