Bergy bit and growler melt deterioration
Bibliographic record
Abstract
The Canadian Ice Service, Environment Canada, is currently developing an operational iceberg forecasting model; the present work forms part of that effort. While existing models predict iceberg drift and deterioration, the new model will account for calving that produces smaller ice pieces and subsequently track the drift and melt of the calved pieces. Bergy bits and growlers, which we consider here to be ice pieces in the size range from 3 to 20 m, can cause large forces upon impact with offshore structures. The probability of encountering these bergy bits and growlers is of significant interest to marine transportation and offshore resource development. Calving due to wave‐induced erosion at the waterline of a floating iceberg can produce many thousands of small ice pieces having a wide distribution of sizes. These small ice pieces then melt as individual entities and eventually disappear. Since the calving events occur periodically, there is a continual supply of small ice pieces in the neighborhood of the parent iceberg. The focus of the present paper is on the evolution of the size‐frequency distribution function for the calved ice pieces. It makes use of the initial distribution function following the calving event discussed by Savage et al. (2000). Dimensional analysis, laboratory tests, and field observations are applied to obtain simple correlations and devise a melt law for the smaller ice pieces. This melt law is then used to determine the temporal evolution of the small ice piece size‐frequency distribution function.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".