Mountain permafrost probability mapping using the BTS method in two climatically dissimilar locations, northwest Canada
Bibliographic record
Abstract
The Basal Temperature of Snow (BTS) method was used to predict permafrost distribution in two climatologically dissimilar mountain environments in northwest Canada. Permafrost probability maps with 30 m × 30 m grid cells were generated for part of the Ruby Range, Yukon Territory (425 km2), and for the Haines Summit area, northern British Columbia (536 km2), using winter BTS measurements in conjunction with late-summer ground truthing by probing and digging pits to physically verify the presence of permafrost. BTS values, and hence permafrost distribution, were modeled using elevation and potential incoming solar radiation (PISR) for the Ruby Range. PISR was not significant at Haines Summit, probably because persistent cloudiness associated with its more maritime climatic regime reduced aspect-induced variability in insolation. Probability maps indicate that ∼66% of the Ruby Range area and ∼43% of the Haines Summit area are underlain by permafrost. Therefore, the Ruby Range should be classified as extensive discontinuous permafrost, while Haines Summit is part of the sporadic discontinuous permafrost zone and not the isolated patches zone as portrayed on recent maps. Extensive ground truthing proved to be an essential part of the procedure because traditional BTS “rules-of-thumb” did not remain valid across the differing mountain climate zones.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.001 | 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".