Mapped winter road connections to remote First Nations communities in Canada 2022-2023, using open data sources and satellite imagery
Bibliographic record
Abstract
This dataset consists of shapefile outlines of winter roads and ice roads in Canada, verified for the 2022-2023 winter road season. It focuses on the public winter roads leading to remote First Nations communities which have no permanent land access. The line data also includes private winter roads, community-built winter roads where information is available, and feeder roads connecting to the permanent road network. First Nations communities connected solely by winter roads are included as point locations. Their local roads were likewise verified, updated, or newly digitised if not included in Canada's National Road Network (NRN) data. Features were traced by hand and information was extracted from Canada's NRN open datasets and then modified using Esri Imagery Basemap, Planet Labs and provincial, municipal and federal information. This dataset aims to provide a temporally and spatially consistent record of varying provincial datasets to support respective infrastructure departments and environmental research of surface and climatic conditions surrounding winter roads. The dataset was produced and funded through a NERC QUADRAT DTP studentship (NE/S007377/1).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.015 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".