Development of a winter road climate risk and vulnerability review framework 2020-2021 update
Bibliographic record
Abstract
During FY2020-2021, a literature review of 54 documents was completed to examine Canada’s winter road network, identify parameters that affect winter road safety, models that assess the impact of climate change on winter roads, and identify standards for climate risk assessment related to northern infrastructure. A framework to review and assess the climate risk and vulnerability of winter roads in Canada was developed. This framework is intended to evolve over time. The foundation of the framework is based on published scientific methods, field data from winter road operations, as well as stakeholder observation, experience and concerns. A case study of the current iteration of the framework was performed on the James Bay Winter Road in Northern Ontario. Consultations with winter road stakeholders within the federal and provincial governments were completed to better understand the unique challenges and perspectives of each stakeholder. An initial review of relevant and available data for the project was completed, and the gaps in data identified.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.113 | 0.083 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.025 | 0.014 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".