Monthly variations of glacier velocity extracted from large scale datasets
Bibliographic record
Abstract
Massive processing using correlation algorithms on optical and SAR image pairs are now largely used to measure glacier surface velocity worldwide. This variable is crucial as it controls glacier mass redistribution and geometry changes. Post-processed products of these raw image-pair velocities are available at an annual scale in open-access. However, at shorter time scales, velocity time-series are still highly uncertain and available at heterogeneous temporal resolutions. This hinders our ability to understand physical processes related to glacier dynamics, such as basal sliding or surges, and the integration of these observations in numerical models. Therefore, post-processing pipelines are needed to extract sub-annual velocity time-series from the large-scale datasets available in open-access or on demand.Here, we introduce an open source and operational Python package called TICOI (Temporal Inversion using Combination of Observations and Interpolation). TICOI is an out-of-core algorithm. It accesses cloud datasets without fully loading them into local memory, and parallelize the processing by chunks, using the dask library. TICOI fuses multi-temporal and multi-sensor image-pair velocities produced by different processing chains, using the temporal closure principle. Several strategies are implemented to improve TICOI robustness to Gaussian noise, temporal decorrelation, and abrupt non-linear changes. Here, we provide extensive examples of TICOI application on the ITS\_LIVE cloud dataset and in-house velocity products. We discuss the performance of our pipeline using GNSS data collected on three glaciers with different dynamics in Yukon and western Greenland. We show that TICOI is able to retrieve monthly velocities even when only annual image-pair velocity observations are available, implying a paradigm shift. Finally, we illustrate the spatio-temporal variations of velocity retrieved by TICOI in several montain range: the Mont Blanc Massif in the Alps, the Qilian Mountains in High Mountain Asia, and the St Elias Mountains in Yukon, Canada.This package opens the door to the regularization of various datasets, enabling the production of standardized sub-annual velocity time-series.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".