Inland Water Body Fraction Map for Canada and Adjacent Regions at 250-m Spatial Resolution
Bibliographic record
Abstract
We present a novel raster dataset of surface inland water body fraction over Canada and neighbouring regions, including the northern parts of the United States, as well as Greenland, Iceland, and the northeastern sector of Russia, at 250-m spatial resolution. It was derived from the Global Surface Water (GSW) dataset (version 5) using a two-step resampling to ensure an accurate replication of the original data and spatial consistency in terms of extent and resolution with the Long-Term Satellite Data Records derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Visible Infrared Imaging Radiometer Suite (VIIRS) sensors. Additional input data and several coastline vector shape databases were utilized to refine the delineation of waterbodies and land-ocean interface. The resulting dataset is an 8-bit signed integer map, where each pixel represents either the water fraction or a land/ocean mask. Positive values indicate water bodies within Canada, while negative values represent areas outside of Canada. This dataset provides a more precise and up-to-date tool for medium-resolution studies of surface inland water in Canada, aligning closely with satellite imagery of similar spatial resolution. The dataset is freely available through the Government of Canada’s Open Government Portal.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".