A COMPARISON BETWEEN CANADIAN DIGITAL ELEVATION DATA (CDED) AND SRTM DATA OF MOUNT CARLETON IN NEW BRUNSWICK (CANADA)
Bibliographic record
Abstract
Digital elevation models (DEM) are basic part of the information about an area. Knowledge about DEM quality is important for their use in management projects, engineering projects and geomorphologic studies. Errors and imprecision of DEM can impact a lot on the resulting models one makes or uses in a project. It’s essential to have accurate topographical information from a DEM.The Centre for Topographic Information (CIT) of Natural Resources Canada produced a particular DEM for the Canada country. These are called Canadian Digital Elevation Data (CDED). The CDED DEM has been used for many types of studies and projects mostly in Canada.The relative accuracy of Canadian Digital Elevation Data of Mount Carleton was assessed using Shuttle Radar Topographic Mission (SRTM) model and profiles/points from Geoscience Laser Altimeter System (GLAS) onboard ICESat. This relative accuracy was examined as a function of surface slope and land cover. Specifically, we analyzed the effect of slope and vegetation type on topographic information (elevation).The particularity of Mount Carleton is that Mount Carleton is the highest mountain in the Maritimes Provinces with the peak at 817 meters and it’s heavily wooded. More than 50 % of the vegetation is dominated by coniferous trees and the average slope is 5.45 ° ± 4.72°. Terrain was segmented into three sloping regions ( ≤ 5°, 5 ° < slope < 15°,> 15°), and also was segmented to aspect regions, standardized to eight geographical directions.From the correlation between CDED and SRTM, we founded a systematic error of less than 2.0 m in absolute value with a standard deviation of around 16 m. We observed that those values are slope-dependent and the influence of their orientation is not significative. A relative influence is observed for the north directions. The broadleaf is the species which has the highest concentration of errors and the obtained rootmean-square
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".