Forecasting the severity of the Newfoundland iceberg season using a control systems model
Bibliographic record
Abstract
The iceberg hazard for the Grand Banks area to the east of Newfoundland varies dramatically from one year to the next. In some years no icebergs penetrate south of 48°N, while in others well over 1000 icebergs enter the main shipping lanes between Europe and NE North America. Advance knowledge of this seasonal hazard would have major implications for ship routing, as well as the resources required for maintaining an effective ice hazard service. Here, a Windowed Error Reduction Ratio control system identification approach is used to forecast the severity of the 2018 iceberg season off Newfoundland, in terms of the predicted number of icebergs crossing 48°N, as well as to hindcast iceberg numbers for 2017. The best estimates are for 766 ± 297 icebergs crossing 48°N before the end of September 2017 and 685 ± 207 for 2018. These are both above the recent observed average of 592 icebergs for that date, and substantially so for 2017. Given the bimodal nature of the annual iceberg number, this means that our predictions for both 2017 and 2018 are for a high iceberg season, with a 71% level of confidence. However, it is most likely that the 2018 iceberg numbers will be somewhat less than 1000, while our higher hindcast for 2017 is consistent with the observed level of 1008. Our verification analysis, covering the 20-year period up to 2016, shows our model's correspondence to the high or low nature of the 48°N iceberg numbers is statistically robust to the 0.05% level, with a skill level of 80%.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".