Modelling permafrost distribution in the Canadian Rocky Mountains: a GIS-based approach
Bibliographic record
Abstract
The vastness in size of permafrost regions in North America, together with the country's harsh climates and rugged terrain, present considerable problems for determining permafrost distribution. Direct methods for determining distribution are expensive and time consuming, so there is a need for indirect methods of prediction. This study addresses the problem of predicting permafrost distribution. It represents a preliminary effort to develop an indirect method of predicting permafrost distribution in a mountainous region of Western Canada. The study area is Plateau Mountain, which is located about 80 km southwest of Calgary, Alberta, Canada in the outer ranges of the Rocky Mountains. The problem is addressed by using spatial analysis conducted within the geographical information system ARC/INFO. Specific criteria used to predict the probable location of permafrost are primarily derived from: 1) a digital elevation model of Plateau Mountain and 2) a landcover classification. The results of the analysis are tested against known locations of permafrost to establish accuracy of the methodology. Results suggest 70% accuracy. This study is the impetus for further development of the methodology to predict permafrost distribution in Jasper National Park, Alberta, Canada - a project currently in progress.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".