Digital elevation model of the Mackenzie River valley, Northwest Territories
Bibliographic record
Abstract
The data represent a digital elevation model (DEM) of the Mackenzie River valley, Northwest Territories, which spans from the Alberta border to the Beaufort Sea (10° of latitude). Contour lines from 1:50 000 digital map sheets (source: < >) for the Mackenzie River valley were edge matched. A line file representing the coastline was also extracted from the waterbodies element of the NTDB and was merged with the contour lines and assigned an elevation of 0 metres. These contour lines were used to generate the DEM. To make the resulting DEM file sizes manageable, the study area is divided in 5 sections. Because of memory limitations, to generate the DEM, each section was subdivided in 5 or 6 parcels. A 30 km buffer around the parcel's boundary was assigned while generating the DEMs with the ANUDEM algorithm in ArcINFO workstation. All contour lines inside the 30 km buffer were used and a minimum Z value of 0 metres was enforced. The resulting DEMs were trimmed by 9 km, then the parcels were mosaicked using a mean. A final step cropped the resulting section to its appropriate neatline (with a 150 m buffer on adjacent sections). Hillshade grids were generated for each section with values of 315º for azimuth, 45º for altitude and 1 for vertical exaggeration (z). The hillshades were used to visually check the quality of the DEM; local filtering was applied where needed. The final DEM sections are named from north to south: mcdem30a, mcdem30b, mcdem30c, mcdem30d, and mcdem30e. Their respective hillshades are named with the "hs" prefix: mcdem30a_hs, mcdem30b_hs, mcdem30c_hs, mcdem30d_hs, mcdem30e_hs.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.021 | 0.005 |
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".