Advancing the Canadian Geospatial Data Infrastructure: Innovations in Automation, Sustainability and Accessible Web Cartography for Mapping the Arctic’s Fragile Ecosystem
Bibliographic record
Abstract
The Canadian Geospatial Data Infrastructure (CGDI) is the collection of geospatial data and the standards, policies, applications, and governance that facilitate its access, use, integration, and preservation for the benefit of and use by all Canadians.To foster innovation, inclusion, interoperability, and sustainability in geospatial data particularly in support of mapping and monitoring the Arctic's fragile ecosystems and to increase the adoption and implementation of standards, Natural Resources Canada's CGDI Division is developing the following products: • Pan-Arctic Wetland Inventory Baseline derived from satellite imagery and ground-truth data using a machine learning and cloud computing classification methodology o Led by Natural Resources Canada, in collaboration with Arctic National Mapping Agencies, Arctic Council and organisations responsible for wetland conservation, a seamless Arctic wetland's dataset over millions © His Majesty the King in Right of Canada, as represented by the Minister of Natural Resources, 2025.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.016 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".