Drift and deterioration of Petermann ice islands
Bibliographic record
Abstract
The eastern Canadian waters is an important part of Canadian shipping lanes and subjected to occasional ice island presence, which could pose serious hazards to offshore and shipping activities in this region. It is, therefore, important to better characterize the dynamics of glacial ice features for safe and cost-effective activities in the region. This thesis presents advanced predictive models to provide a better understanding of how atmospheric and oceanic variables influence ice island drift and deterioration. To understand how ice islands drift under the influence of atmospheric and oceanic forces, a deterministic model was presented, where the relative contribution of various forces governing the drift of four tracked ice islands was quantified. The results showed that in low sea ice concentrations and ice island speeds, ocean current and sea surface tilt forces dominated ice island force balance (63% on average). Wind, however, played a minor role (< 5%), and Coriolis and sea ice forces were significant only at higher ice island speeds and sea ice concentrations, respectively. Atmospheric and oceanic variables were further studied using a probabilistic Bayesian approach to investigate their relative influences on the fracture events and drift velocities of hundreds of Petermann ice islands tracked in the Canadian Ice Island Drift, Deterioration and Detection database. The presented models identified water temperature and ocean currents as the most important contributor to ice island fracture events and drift velocities, respectively. It was revealed that under severe conditions of wind, current, waves, and air/water temperatures, ice islands are more likely to fracture, with fracture probability reaching as high as 75% in extreme conditions. It was also revealed that under stronger currents, ice islands are most likely to drift at higher speeds and in close proximity to ocean current direction. Models were validated using the 5-fold cross-validation approach and errors up to 39% and 29% were reported in the fracture and drift probability estimations, respectively. The presented models have predictive capabilities for future drift and deterioration of Petermann ice islands. However, further training and testing of the developed models is necessary before they can be used as operational forecasting tools.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".