Icebergs, Genealogy and Jigsaw Puzzles
Bibliographic record
Abstract
Accurate estimates of iceberg populations, disintegration rates and iceberg movement are essential to understand ice sheet contributions to global sea level change, effects of freshwater inputs on ocean circulations and heat balances. Furthermore, there are operational imperatives to predict iceberg drift and fragmentation in order to ensure the safety and efficiency of polar shipping. The dynamics, persistence, fragmentation rates, melt rates and dispersal of icebergs are, however, poorly understood due to a lack of automated approaches for monitoring them.We present an automated iceberg tracking approach that is capable of reconstructing iceberg paths, fragmentations and ultimately lineages through multiple generations based on satellite radar imagery. The method offers scope for the first time to relate iceberg fragments back to their original source computationally, which will allow scalable deployment and the development of improved predictive iceberg drift and disintegration models and a better understanding of contributions to nutrient and freshwater distributions. Tracking is developed using the Canadian Ice Island Drift, Deterioration and Detection (CI2D3) database. This contains manually-delineated observations of large tabular icebergs in the Canadian Arctic between 2008 and 2012 based on RADARSAT-1 and -2 imagery. Critically, CI2D3 documents the lineages of icebergs across fragmentation events and therefore provides a unique ground control dataset allowing evaluation of tracker performance.Tracking of unchanging icebergs is achieved using a Bayesian tracking algorithm that makes linkages based upon a variety of geometric shape descriptors. Tracking across fragmentation events minimises Dynamic Time Warping distances between residual perimeter curves for candidate fragments and potential parents. This enables the matching of noisy, partial geometries and the automatic tessellation of fragments at one time step into the outline of their parent in a preceding observation irrespective of the intervening drift patterns. We evaluate tracker performance against bespoke metrics and those developed for cell tracking challenges that include mitotic division.The system provides a generalisable geospatial tracking methodology based on object geometries that is applicable to other contexts and questions as well as a novel means of reconciling global invariances in geometries when conducting shape fingerprinting and matching.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".