Predicting sea ice conditions for marine operations in ice-covered waters
Bibliographic record
Abstract
Marine operations in ice-covered waters require reliable and timely information about the sea ice conditions. The Canadian Ice Service produces and distributes the ice information to mariners operating in the Canadian water in the form of daily ice charts. Unfortunately, however, due to the time difference between the production and the use of the ice charts, the ice information is always out of date, which endangers the safety of marine operations. To efficiently overcome this problem, a reliable model for predicting the sea ice conditions (concentrations) over time is developed. Inspecting the ice charts for the period 1987 to 1998 showed that the sea ice conditions change according to a regular pattern to some extent. Therefore, a neutral network function approximation system could model, and hence predict, these changes efficiently when trained using multiple-year ice concentrations readings. The data used in training the neural network are extracted from the ice charts for the Gulf of St. Lawrence in eastern Canada. The input to the network is a vector which represents the current ice concentrations over a test area containing 40 points. The input vector is mapped to an output vector that gives the predicted ice concentrations.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 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".