A new method for deriving glacier centerlines applied to glaciers in Alaska and northwest Canada
Bibliographic record
Abstract
Abstract. This study presents a new method to derive centerlines for the main branches and major tributaries of a set of glaciers, requiring glacier outlines and a digital elevation model (DEM) as input. The method relies on a "cost grid–least-cost route approach" that comprises three main steps. First, termini and heads are identified for every glacier. Second, centerlines are derived by calculating the least-cost route on a previously established cost grid. Third, the centerlines are split into branches and a branch order is allocated. Application to 21 720 glaciers in Alaska and northwest Canada (Yukon, British Columbia) yields 41 860 centerlines. The algorithm performs robustly, requiring no manual adjustments for 87.8% of the glaciers. Manual adjustments are required primarily to correct the locations of glacier heads (7.0% corrected) and termini (3.5% corrected). With corrected heads and termini, only 1.4% of the derived centerlines need edits. A comparison of the lengths from a hydrological approach to the lengths from our longest centerlines reveals considerable variation. Although the average length ratio is close to unity, only ~ 50% of the 21 720 glaciers have the two lengths within 10% of each other. A second comparison shows that our centerline lengths between lowest and highest glacier elevations compare well to our longest centerline lengths. For > 70% of the 4350 glaciers with two or more branches, the two lengths are within 5% of each other. Our final product can be used for calculating glacier length, conducting length change analyses, topological analyses, or flowline modeling.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".