Icings in the Great Slave region (1985-2014), Northwest Territories, mapped from Landsat Imagery
Bibliographic record
Abstract
Icings are sheet-like masses of layered ice that form over the winter by freezing of successive flows of water on the ground surface or on top of river or lake ice. Because icings can negatively impact the performance of seasonal and all-season roads, they are a transportation risk in the Arctic. Therefore, maps of their occurrence and reoccurrence provide important geoscience information required for development and transportation infrastructure planning. In this study, threshold values of band ratios were used to derive a set of icing maps from Landsat image time series of images (1985 - 2014), located within the Slave Geological Province (WRS-2 Path 47/Row 16). The icings maps were generated using image data acquired in late-spring when the region is largely snow-free, but ice bodies remain. A water mask created from summer image data was used to differentiate frozen water bodies so any remaining ice was considered to be land-fast, and thus icings formed from winter overland flow of water. Icing occurrence and reoccurrence maps were generated by overlaying the successive icing distribution maps in a Geographic Information System. This Open File contains digital, georeferenced icing data.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".