Winter Roads and Ice Bridges: Anomolies in Their Records of Seasonal Usage and What We Can learn From Them
Bibliographic record
Abstract
Most believe that the warming of the earth's climate system is unequivocal, as is now evident from observations of increases in global average air and ocean temperatures, widespread melting of snow and ice and rising global average sea level. One possible effect for winter roads and ice bridges in Canada's North is that the mean length of time each road or ice bridge is operationally open will progressively shorten. Tracking the recorded start- and end-of-season dates and thus the length of season over time for each confirms that - for most cases. But in some cases, the recorded seasonal length has actually increased over time. Global warming will continue to be imposed on Canada. Clearly there are lessons to be learned from the case histories of those winter roads and ice bridges that saw increased seasonal usage. These lessons could be applied to other roads and ice bridges to great benefit, to adapt to the changing climate. The paper provides the detailed statistical record of the seasonal usage of selected winter roads and ice bridges in the Northwest Territories, identifying those cases where the length of the operational season has increased. It then examines what may have caused the lengthening of the seasonal usage of those winter roads and ice bridges, in the face of climate change.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".