Derivation and analysis of a complete modern-date glacier inventory for Alaska and northwest Canada
Bibliographic record
Abstract
Abstract We present a detailed, complete glacier inventory for Alaska and neighboring Canada using multi-sensor satellite data from 2000 to 2011. For each glacier, we derive outlines and 51 variables, including center-line lengths, outline types and debris cover. We find 86 723 km2of glacier area (27 109 glaciers >0.025 km2), ∼12% of the global glacierized area outside ice sheets. Of this area 12.0% is drained by 39 marine-terminating glaciers (74 km of tidewater margin), and 19.3% by 148 lake- and river-terminating glaciers (420 km of lake-/river margin). The overall debris cover is 11%, with considerable differences among regions, ranging from 1.4% in the Kenai Mountains to 28% in the Central Alaska Range. Comparison of outlines from different sources on >2500 km2of glacierized area yields a total area difference of ∼10%, emphasizing the difficulties in accurately delineating debris-covered glaciers. Assuming fully correlated (systematic) errors, uncertainties in area reach 6% for all Alaska glaciers, but further analysis is needed to explore adequate error correlation scales. Preliminary analysis of the glacier database yields a new set of well-constrained area/length scaling parameters and shows good agreement between our area–altitude distributions and previously established synthetic hypsometries. The new glacier database will be valuable to further explore relations between glacier variables and glacier behavior.
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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 | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".