Assessment of approaches and costs associated with the correction of the HRDEM product data in the Canadian Arctic
Bibliographic record
Abstract
The High-Resolution Digital Elevation Model (HRDEM) was created as part of the National Elevation Data Strategy to provide detailed elevation data across the country. For the Canadian Arctic, the HRDEM was based on the ArcticDEM initiative with additional post-processing by the Canada Centre for Mapping and Earth Observation to create a 2-meter Digital Surface Model (DSM) covering a geographic region of approximately 4.6 million km2. This report provides details on the investigation of the data issues within HRDEM in the North, available open and commercial sources of elevation data that could be used to improve the product, and technologies available to generate high resolution DSM at similar levels of accuracy and resolution than the current HRDEM. In addition, the report summarizes the results of a research into the common, as well as more advanced (e.g., machine learning), methods for improving the product. In summary, the intent of this investigation was to provide supporting information to address the data anomalies in HRDEM and present a path forward.
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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 | 0.010 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".