Modernization of Canadian Digital Topographic Data
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
Canadians have been without a current Canada-wide digital terrain model since the prior product was not updated since 2011.Natural Resources Canada's elevation data team has been active with the acquisition of high-resolution data, leveraging lidar data and optical satellite imagery.However, this initiative will take several more years before achieving complete coverage across all of Canada's land mass.Satellite derived elevation models are numerous, providing global coverage.These models are surface models, containing vegetation and man-made infrastructure.Many applications require a terrain model, which represents 'bare-earth' elevation.In this project, we started with a global surface model, i.e.Copernicus GLO-30, and performed numerous spatial operations to extract a terrain model from it, through the inclusion of auxiliary datasets such as settled areas and forest heights, to create a modified Copernicus terrain model.Data fusion was then performed to include down sampled lidar-derived highresolution terrain data where available, as this data is of better vertical accuracy.The result is a new, modern, and supported 30m resolution raster, which will be updated as new lidar data are released.The vertical accuracy of the 30m MRDTM is compared to the high-resolution data, before it's fused into the MRDTM, and to a variety of RTK control points in vegetated and non-vegetated regions.Results indicate the MRDTM is a significant improvement over the previously available national elevation datasets.
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 | 0.000 |
| 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