An Operational Thermodynamic-Dynamic Model for the Coastal Labrador Sea Ice Melt Season
Bibliographic record
Abstract
Abstract. An offshore operations thermodynamic-dynamic prediction model of sea ice break-up and drift is presented for central coastal Labrador in Atlantic Canada, and demonstrated for portions of the 2015 spring break-up of the land-fast ice. The model validation is performed using the data from ice tracking buoys deployed on the land-fast ice, which began drifting after break-up of the land-fast ice. The model uses a one-dimensional thermodynamic parameterization for ice melt and growth, includes snow accumulation and melt, and melt-pond and lead growth and contraction. The dynamic model uses a Smoothed Particle Hydrodynamics (SPH) parameterization for ice motion and changes in ice thickness and concentration. The dynamic forcing parameters include wind and ocean current drag, Coriolis deflection, internal ice stresses, and gravitational forcing due to sea surface gradients. A coastal repulsion force is employed to prevent ice particles from crossing the coastal boundaries. The model is sensitive to the prescribed initial snow depth on the sea ice. In the present work, analysis of results is focused on the offshore regions of Makkovik and Nain, Labrador. The melt of the coastal land-fast ice in these regions can be adequately simulated by the thermodynamic model alone. The model predicts the timing of the local land-fast ice break-up to within 4.6 hours to six days, and can simulate observed ice buoy drift speeds to within 1.5 meters per second.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".