A Permafrost Probability Model for the Southern Yukon and Northern British Columbia, Canada
Bibliographic record
Abstract
ABSTRACT Permafrost maps are needed for infrastructure planning, climatic change adaptation strategies and northern development but often lack sufficient detail for these purposes. The high‐resolution (30 x 30 m grid cells) probability model for the southern Yukon and northern British Columbia presented in this paper (regional model) is a combination of seven local empirical‐statistical models, each developed from basal temperature of snow measurements in winter and ground‐truthing of frozen‐ground presence in summer. The models were blended using a distance‐decay power approach to generate a map of permafrost probability over an area of almost 500 000 km2 between 59°N and 65°N. The result is broadly similar to previous permafrost maps with an average permafrost probability of 58 per cent for the region as a whole. There are notable differences in detail, however, because the main predictive variable used in the local models is equivalent elevation, which incorporates the effects of gentle or inverted surface lapse rates in the forest zone. Most of the region shows permafrost distribution patterns that are non‐linear, resembling those from continental areas such as Mongolia. Only the southwestern area shows a similar mountain permafrost distribution to that in the European Alps with a well‐defined lower limit and a linear increase in probability with elevation. The results of the modelling can be presented on paper using traditional classifications into permafrost zones but given the level of detail, they will be more useful as an interactive online map. Copyright © 2012 John Wiley & Sons, Ltd.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".