A statistical analysis on thawing permafrost in Canada’s north and its effects on housing
Bibliographic record
Abstract
Thawing permafrost has been exacerbated by the effects of climate change and lack of action. With an estimated permafrost loss of 40% by the end of the century, northern Canadian regions, which are home to over 120,000 residents, are becoming increasingly vulnerable to its numerous consequences. This report explored the ecology behind permafrost and the environmental and infrastructure implications of thawing both long and short term. A statistical analysis using Python data visualization was used to explore permafrost patterns in southern Yukon and the Northwest Territories. Data science, scholarly research, and expert outreach were employed for a holistic overview. The report used open-access data from the National Environment Research Council’s (NERC) Environmental Information Data Centre to analyze the soil temperature, soil depth, thaw depth, and its correlation to infrastructure vulnerability. Google Colaboratory was used to perform statistical analyses and the chosen language was Python. Pyplot and Pandas were used to import and graph the raw data for data visualization. The data, in the form of CSV files, were cleaned using tokenism, stemming, and other functions. Statistical analyses were used to ensure the significance of this research. Scholarly research was performed using electronic databases on Nature, Sustainability, ScienceDirect, JSTOR, and ProQuest. A discussion was made based on the findings presented to conclude the urgency of the issue from a range of perspectives, possible solutions to mitigate the impacts of thawing permafrost on infrastructure, and future research steps.
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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 | 0.009 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".