TICOI: an operational Python package to generate regularized glacier velocity time series
Bibliographic record
Abstract
Abstract. Ice velocity is a crucial observation as it controls glacier mass redistribution and future geometry. While glacier annual velocities are now available in open-source worldwide, sub-annual velocity time series are still highly uncertain and available at heterogeneous temporal resolutions. This hinders our ability to understand flow processes, such as basal sliding or surges, and integration of these observations in numerical models. We introduce an open source and operational Python package called TICOI (Temporal Inversion using Combination of Observations and Interpolation). TICOI fuses multi-temporal and multi-sensor image-pair velocities produced by different processing chains, using the temporal closure principle. In this article, we provide extensive examples of TICOI application on the ITS_LIVE dataset and in-house velocity products. The results are validated using GNSS data collected on three glaciers with different dynamics in Yukon and western Greenland, including a surging glacier. Comparison with GNSS observations demonstrates a reduction in error by up to 50 % in comparison with the raw image-pair velocities and other post-processing methods. This increase in performance comes from the development of methodological strategies to enhance TICOI's robustness to temporal decorrelation and abrupt non-linear changes. TICOI also proves to be able to retrieve monthly velocity when only annual image-pair velocities are available. This package opens the door to the regularization of various datasets, enabling the creation of standardized sub-annual velocity products.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 0.012 |
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".