Thermal requirements for surviving a mass rescue incident in the arctic - report on environmental conditions
Bibliographic record
Abstract
In this project, the objective is to investigate if the current thermal protective equipment and preparedness available to people traveling in the Canadian Arctic are adequate for surviving a major air or cruise ship disaster and to identify the minimum thermal protection criteria for survival. To facilitate experimental design and modeling effort, the environmental conditions along the cross-polar routes for modern aviation and shipping routes for cruise ships in the Canadian Arctic are studied and summarized in this report. There is daily air traffic through the Arctic, so it is important to consider mean and extreme environmental conditions throughout the year as accidents may occur any time. However, cruise ship traffic through the Canadian Arctic is typically between the months of July to late September or early October, where the environmental conditions are less severe. Considering air traffic throughout the year, the daily average air temperature varies between 5°C and -40°C. The extreme minimum air temperature can drop to -55°C and the extreme wind chill can be 70°C. Snow depth can be up to 40 cm during winter months. For cruise traffic during the months of July to September, the daily average air temperature varies between 5°C and 10°C. In July, there is typically one day or less with temperature below –2°C. In August, there are typically 5 to 10 days below –2°C but not less than 10°C. In September, virtually every day is below 2°C. There are 5 to 10 days below 10°C and approximately 3 days below 20°C. In the summer, typical significant wave height reaches 3 m and peak period varies between 4.5 s and 20 s.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".