Descriptor: Medium Resolution Digital Elevation Model From Natural Resources Canada’s CanElevation Series (MRDEM-30)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it