Probability mapping of mountain permafrost using the BTS method, Wolf Creek, Yukon Territory, Canada
Bibliographic record
Abstract
Abstract The basal temperature of snow (BTS) method was used to predict the distribution of permafrost within a mountainous basin located in the southern Yukon Territory. A modelled BTS surface, based on several hundred measured values, was created within a Geographic Information System (GIS) environment using elevation and potential incoming solar radiation as independent variables. The distribution of frozen ground at 200 test sites was compared to the modelled BTS values using logistic regression. The resultant map of permafrost probability shows that all four conventional permafrost distribution classes (isolated patches, scattered and widespread discontinuous permafrost, and continuous permafrost) are present within the basin. Supplementary logistic regression analyses reveal that at certain elevations and aspects, the probability of permafrost occurrence varies markedly over short distances in response to snowpack depth. They also show that widespread alterations in snow cover would be expected to substantially affect permafrost distribution even if air temperatures were to remain unchanged. Copyright © 2004 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".