Long-Range Prediction of the Shipping Season in Hudson Bay: A Statistical Approach
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Bibliographic record
Abstract
Abstract Despite recent reductions in Arctic sea ice extent and the associated increase in both the recreational and commercial use of ice-infested waters, long-range prediction of operationally relevant sea ice parameters is an area of seasonal forecasting that has received little attention. Statistical methods that isolate and exploit empirical relationships between antecedent low-frequency climate variability and specific variables of interest are often used to solve seasonal forecasting problems. In this study, simple multiple linear regression (MLR) techniques are used to improve the skill of the seasonal (3-month lead) forecast of the breakup and clearing of sea ice along the shipping route through Hudson Bay that is issued each March by the Canadian Ice Service of Environment Canada. Using sea ice and climate data from 1972 to 2002, predictive MLR models are developed for the spring opening date of the shipping route and the latest expected opening date. A success rate of 77% over the 1972–2002 period for the opening date, from an MLR model that explains 76% of the variability in the original time series with a mean absolute error (MAE) of 0.38, is a marked improvement over the 48% success rate of the current analog methodology. The success rate of the model for the latest expected date is 87%; the modeled time series adequately represented interannual variability in the observed time series (r = 0.71) with a low MAE (MAE = 0.51). Results from a series of model diagnostics that include Monte Carlo simulations, cross validation, and analysis of residuals, suggest the final models are statistically valid and are not influenced by artificial skill. The main source of predictive skill in the model is winter low-frequency variability in North Atlantic sea surface temperatures and 500-mb geopotential heights; physical processes that may explain this link are presented. It is concluded that simple multiple linear regression techniques can be applied to generate use-specific seasonal forecasts of sea ice conditions and that the empirical knowledge gained in the model development may help elucidate or identify physical processes in the climate system.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it