Long-range forecasting of the ice break-up dates for the Yukon River by the synoptic statistical method
Bibliographic record
Abstract
A scheme for obtaining a long-range forecast of the dates of ice break-up is proposed for the Yukon River (North America). The scheme is based on a well-proven national practice of ice forecasting, namely, on the meteorological statistical method. The method utilizes a linear dependence of the predicted value on the characteristics of temperature and pressure fields in the North Atlantic and the North Pacific. The most informative predictors are selected. Statistical stability of the forecast formula parameters is verified. The average forecast lead time is 40 days. The verification of the proposed methodology performed for three stretches of the Yukon River on the basis of independent data for the period from 2009 to 2015 showed that it allows obtaining quite satisfactory results with a fairly low root-mean-square error and a fairly high accuracy of forecasts. Keywords: river ice break-up, long-range forecast, synoptic statistical method, temperature and pressure fields, predictors, stability, method verification
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".