Changes in Fog, Ice Fog, and Low Visibility in the Hudson Bay Region: Impacts on Aviation
Bibliographic record
Abstract
Fog and low visibility present a natural hazard for aviation in the Hudson Bay region. Sixteen communities on the eastern and western shores of Hudson and James Bays, Canada, were selected for fog, ice fog, and low visibility statistical analyses for a range of 21 to 62 year time series. Both fog hours and ice fog hours were found to be in general decline, with some locations experiencing statistically significant declines. Spatial asymmetries for fog and ice fog were observed among the various areas within the Hudson Bay region. The more northerly locations in this study experienced statistically significant declines in fog hours while the southerly locations’ declines were not significant. Fog was significantly declining in some western Hudson Bay locations during spring and fall and in James Bay during winter and summer, but minimal trends were observed in eastern Hudson Bay. For ice fog hours, all of the locations in the western shore of Hudson Bay experienced a significant decline in winter while only one-third of the locations in eastern shores were found to be declining significantly during winter. Blowing snow, snow, ice and fog were the leading causes for reduced and low visibilities at the majority of the locations. Other factors such as rain contributed a minor role to low visibility.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".