Synthesized Inventory of Relevant Infrastructure and Utilized Areas, Including Contaminated Sites (SIRIUS)
Bibliographic record
Abstract
The SIRIUS inventory integrates data from OpenStreetMap for infrastructure and land use information (OpenStreetMap Contributors and Geofabrik GmbH, 2018), the modeled Northern Hemisphere permafrost map by Obu et al. (2018), the contaminated sites database and reports by the State of Alaska Department of Environmental Conservation (2023) (DEC), the inventory of contaminated sites in Canada by the Treasury Board of Canada Secretariat (FCSI) (2024), a compilation of contaminated sites in Greenland and Norway from the NGO Robin des Bois (2009a, 2009b) (RdB), projections of the timing of first talik formation, a model output from the CryoGridLite permafrost model (Langer et al., 2024) for the Arctic regions of Alaska (AK), Canada (CA), Greenland (GL), and Norway (NO). This dataset is provided as a GeoPackage, enabling seamless integration with spatial databases (e.g., PostgreSQL/PostGIS), Geographic Information Systems (e.g., QGIS), and geospatial processing libraries (e.g., Python's GeoPandas). All layers can be queried either independently or in combination with one another. The GeoPackage contains 17 separate layers. The contaminated sites layers contain detailed information on contaminants haromized into chemical classifications based on the Agency for Toxic Substances and Disease Registry (ATSDR). Additionally, each contaminated sites layer includes projections of talik formation as complementary attributes. These projections are provided for SSP1-2.6 and SSP5-8.5 scenarios, specifying the 5th, 50th (median), and 95th percentiles of the year when talik formation is expected to occur for the first time. The layers of the infrastructure and human-impacted areas are further divided into points of interest (POI), polygonal infrastructure, and linear road and rail networks (RRNetwork) for each Arctic region (AK, CA, GL, and NO). The permafrost extent is provided as a single pan-Arctic layer. Contaminated Sites: AK_ContaminatedSites_DEC CA_ContaminatedSites_FCSI GL_ContaminatedSites_RdB NO_ContaminatedSites_RdB Infrastructure and Human-Impacted Areas: NO_POI_InfrastructureHIElements_OSM NO_InfrastructureHIElements_OSM NO_InfrastructureHIElements_RRNetwork_OSM GL_POI_InfrastructureHIElements_OSM GL_InfrastructureHIElements_OSM GL_InfrastructureHIElements_RRNetwork_OSM AK_POI_InfrastructureHIElements_OSM AK_InfrastructureHIElements_OSM AK_InfrastructureHIElements_RRNetwork_OSM CA_POI_InfrastructureHIElements_OSM CA_InfrastructureHIElements_OSM CA_InfrastructureHIElements_RRNetwork_OSM Permafrost Extent: PermafrostZones_UiO Download Data pan-arctic_SIRIUS_v1.0.gpkg
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.008 |
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".