Perspectives on the production of a glacier inventory from multispectral satellite data in Arctic Canada: Cumberland Peninsula, Baffin Island
Bibliographic record
Abstract
Abstract The consequences of global warming on land ice masses are difficult to assess in detail, as two-dimensional glacier inventory data are still missing for many remote regions of the world. As the largest future temperature increase is expected to occur at high latitudes, the glaciers and ice caps in the Arctic will be particularly susceptible to the expected warming. This study demonstrates the possibilities of space-borne glacier inventorying at a remote site on Cumberland Peninsula, a part of Baffin Island in Arctic Canada, thereby providing glacier inventory data for this region. Our approach combines Landsat ETM+ and Terra ASTER satellite data, an ASTER-derived digital elevation model (DEM) and Geographic Information System-based processing. We used thresholded ratio images from ETM+ bands 3 and 5 and ASTER bands 3 and 4 for glacier mapping. Manual delineation of Little Ice Age trimlines and moraines has been applied to calculate area changes for 225 glaciers, yielding an average area loss of 11%. A size distribution has been obtained for 770 glaciers that is very different from that for Alpine glaciers. Numerous three-dimensional glacier parameters were derived from the ASTER DEM for a subset of 340 glaciers. The amount of working time required for the processing has been tracked, and resulted in 5 min per glacier, or 7 years for all estimated 160 000 glaciers worldwide.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".