Developments to 2D modelling of ice island deterioration using the CI2D3 Database
Bibliographic record
Abstract
The calving of ice tongues and ice shelves can generate large, tabular icebergs that have climatological implications given their role in dispersing freshwater from the Greenland and Antarctic ice sheets. These ‘ice islands’ also pose potential risk to marine industry. It is therefore critical that influential deterioration mechanisms be accurately represented in simulations of ice island drift and deterioration, both for risk mitigation in offshore industry and for climatological studies that are focused on the Polar Regions. The majority of ice island deterioration is the result of sidewall erosion, and specifically that which results from waterline wave-erosion leading to ram growth and buoyancy-forced fracture. This study therefore focuses on the inclusion of the buoyancy-driven “footloose” calving mechanism (Wagner et al., 2014) in simulations of ice island length and areal change. Using size and lineage information of ice islands tracked in the Canadian Ice Island Drift, Deterioration and Detection (CI2D3) Database, we quantitatively assess the performance of the footloose calving model by simulating the deterioration of 172 ice islands. The mean model error was +15 (+/- 400) m over 20 d and increased to +401 (+1400/-800) m for simulations that ran to 80 d. The performance of the footloose calving model is a substantial improvement when compared to simulations that did not include this calving mechanism. For example, a thermal-melt model had mean errors of -252 and -1403 m at 20 and 80 d of simulation, respectively, and the mean error of a zero-melt model was -281 and -1545 m over the same time periods. We also present a new approach to modelling ice island areal change resulting from footloose calving. This simple, two-parameter approach simulates discrete footloose calving events and adjusts the ice island surface area to maintain a constant aspect ratio. Mean model error remained under 1 km2 over 80 d of simulation, showing that the model performs well over numerous months. Using the CI2D3 Database, we were able to conduct the first large-scale assessment of the footloose model’s performance in simulating change to the ice island length dimension. The morphological data included in the database also provided the opportunity to develop an approach for modelling areal deterioration resulting from footloose calving events. The model assessments would benefit from more observations of long-duration ice island tracks, as there were a limited number of ice islands that were tracked in the CI2D3 Database for over 40 d. Future work can look to implement the presented approaches in operational and climatological modelling while the iceberg modelling community also develops an approach to simulate larger-scale ice island fracture.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".