A new glacier inventory on southern Baffin Island, Canada, from ASTER data: II. Data analysis, glacier change and applications
Bibliographic record
Abstract
Abstract Despite its large area covered by glaciers and ice caps, detailed glacier inventory data are not yet available for most parts of Baffin Island, Canada. Automated classification of satellite data could help to overcome the data gaps. Along-track stereo sensors allow the derivation of a digital elevation model (DEM) and glacier outlines from the same point in time, and are particularly useful for this task. While part I of this study describes the remote-sensing methods, in part II we present an analysis of the derived glacier inventory data for 662 glaciers and an application to glacier volume and volume-change calculations. Among other things, the analysis reveals a mean glacier elevation of 990 m, with a weak dependence on aspect and a close agreement of the arithmetic mean with the statistical mean elevation as derived from the DEM. A strong scatter of mean slope is observed for glaciers <1 km2, and the derived glacier thickness differs by a factor of two for glaciers of the same size. For the period from about 1920 to 2000 the relative area change is –12.5% (264 glaciers), with a strong dependence on glacier size. Mean mass loss as derived from volume changes is about –0.15 mw.e. a–1.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".