Predicting Sea Ice Conditions using Neural Networks
Bibliographic record
Abstract
Safe and efficient marine navigation in ice-infested waters requires comprehensive and timely information on the sea ice conditions. These include information on the ice concentration and type, ice edge location, icebergs and open leads. The Canadian Ice Service is responsible for providing ice information in Canadian waters, mainly through its daily ice charts. Unfortunately, due to the difference in time between the ice chart production and its use by mariners, the ice information is always out of date. This problem might be overcome by developing a neural network-based system for predicting the ice conditions over time. A supervised neural network is trained to predict the ice conditions at a given location and time using the current ice charts, which are provided by the Canadian Ice Service. The input ice data is mapped to an output vector that gives the predicted ice conditions. The traditional non-modular feed-forward neural network structure failed to map the required function, and hence, was modularized to give better prediction performance. Each neural module was responsible for the prediction of a 5×5 km area, while the ice characteristic of interest was the total concentration.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".