Initial validation of Randolph Glacier inventory: version 5.0 data over Canada using 250 m MODIS-derived annual minimum snow/ice extent
Bibliographic record
Abstract
This report describes the background, methodology and results of using annual Minimum Snow and Ice (MSI) extent derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) 250 m data to validate glacier outline data from the Randolph Glacier Inventory: Version 5.0 (RGI 5.0). This work was a four part collaborative effort conducted by 1) a team from the Canada Centre for Remote Sensing (CCRS) who produced the MODIS MSI raster data and worked with the Atlas of Canada Data (Atlas Data) team to facilitate the use of the raster imagery, 2) the CCRS GeoAnalytics team who evaluated sources of glacier data, 3) the Atlas Data team who carried out the classification and vectorization of the MODIS raster imagery and the validation of the RGI 5.0 glaciers and 4) the Geological Survey of Canada (GSC) who advised on the interpretation of Google Earth and LANDSAT 8 OLI_TIRS image products that were used as references. In particular, it was observed that seventeen glaciers with an area greater than 2.0 km2 are suspected of having either fully or significantly melted. They are distributed across northern Canada, with four located in the Yukon, seven located in Arctic Canada South region and six located in Arctic Canada North region. The validated glacier data will be generalized to the 1:1,000,000 scale and used as a national scale dataset for Canadian glaciers.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".