Short-Term Operational Sea Ice Forecasting for Arctic Shipping
Bibliographic record
Abstract
The Arctic sea ice is declining in extent, volume and thickness. With this decline comes an increased interest in the two main Arctic shipping routes: Canada’s Northwest Passage (NWP) and Russian Northern Sea Route (NSR). The NWP is the most direct route between Asia and the East coast of North America. Some routes are up to 40% shorter than those using the Suez Canal. With commercial and contractual implications, Arctic shipping route access needs to be predictable with sufficient lead time to allow optimization. This paper presents a methodology for forecasting the timing and length of the open-water season (by determining freeze-up and break-up dates) on regional scales at key locations in the NWP along with examples of applications. A suite of statistical models were developed to forecast the timing and length of the open-water season at key locations within the NWP, using a multi-node based quadratic discriminant (QD) approach. Forecasts are feasible up to four weeks in advance. Ensembles of QD models were built for key regions using a feature selection method to select an optimized set of input parameters to better discriminate between two states (i.e., ice or open-water). The set of available features used included observed and modeled environmental, oceanographic and atmospheric parameters. Results of models with a 28-day forecast horizon show that over 59% of predictions for break-up and 79% of predictions for freeze-up fall within a ±4-day range, which is the error on the reference dates derived from the weekly CIS ice charts.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".