Icebergs, jigsaw puzzles and genealogy: Automated multi-generational iceberg tracking and lineage reconstruction
Bibliographic record
Abstract
Abstract. Tabular icebergs calve from ice shelves and glaciers in Antarctica, Greenland, and northern Ellesmere Island. These ‘ice islands’, as they are referred to in the Arctic, drift, melt, and fragment, contributing fresh water and nutrients to the ocean, influencing circulation, carbon cycling and biodiversity in ways that remain poorly understood. Icebergs also pose risks to shipping, and maritime infrastructure. Improved understanding of iceberg drift and fragmentation will reduce uncertainties in climate simulations and operational hazards. This study presents the first comprehensively validated, scalable iceberg tracking approach and the first that is capable of reconstructing iceberg ‘lineages’ (here used to describe life histories including sources, where that source is a larger iceberg) through fragmentation events. This method enables a comprehensive reconstruction of iceberg paths from calving to their eventual disintegration, allowing for monitoring and source attribution across their life cycle. We propose CryoTrack, an unsupervised approach based on iceberg geometry that is agnostic to data source or delineation method. The system requires only vector outlines. Initially, icebergs are linked across timesteps when their shapes remain similar, forming ‘tracklets’. When significant shape changes occur, fragmented ‘child’ icebergs are linked to their ‘parents’ using a fuzzy geometric assembly method based on dynamic time warping, akin to assembling a jigsaw puzzle without image data. This approach reconstructs full iceberg lineages back to their calving origin. We evaluate system performance using manually tracked iceberg outlines in the Canadian Arctic. Standard tracking metrics and custom iceberg specific metrics assess its accuracy in scientific and operational contexts. Our approach achieves excellent tracking of icebergs with an overall tracking accuracy of 0.98 and 94 % of iceberg area are correctly linked to source when icebergs are last observed. This system contributes to the need for scalable iceberg monitoring. It enhances understanding of iceberg behaviours, impacts, and fragmentation, supporting process based and data driven predictive modelling for environmental and operational applications.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".