GLAcier Feature Tracking testkit (GLAFT): a statistically and physically based framework built on top of open science workflows for evaluating glacier velocity maps
Bibliographic record
Abstract
Accurate assessments of glacier velocity are essential for understanding ice flow mechanics, monitoring natural hazards, and projecting future sea-level rise. However, the most commonly used method for deriving glacier velocity maps, known as feature tracking, relies on empirical parameter choices that rarely account for glacier physics or uncertainty. The GLAcier Feature Tracking testkit (GLAFT) aims to assess velocity maps using two statistically and physically based metrics. Velocity maps with metrics falling within our recommended ranges contain fewer erroneous measurements and more spatially correlated noise than velocity maps with metrics that deviate from those ranges. Consequently, these metric ranges are suitable for refining feature-tracking workflows and evaluating the resulting velocity products. GLAFT provides modulized workflows for calculating these metrics and the associated visualization, facilitating the velocity map assessments. To ensure the package is available, reusable, and redistributable to the maximum extent, GLAFT adopts several open science practices including the narrative documentation and demos using Jupyter Book and cloud access using Ghub. By providing the benchmarking framework for evaluating the quality of glacier velocity maps procedure, GLAFT enables the cryospheric sciences and natural hazards communities to leverage the rich glacier velocity data now available, whether they are sourced from public archives or made through custom feature-tracking processes.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.014 |
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".