Scenario-based climate change modelling for a regional permafrost probability model of the southern Yukon and northern British Columbia, Canada
Bibliographic record
Abstract
Abstract. Scenario-based climate change modelling for equilibrium conditions was applied to a Regional Model of permafrost probability for the southern Yukon and northwestern British Columbia. Under a −1 K cooling scenario, permafrost area expands from 58% (present day) of the 490 000 km2 to 76%, whereas warming scenarios of +1 K, +2 K and +5 K decrease the terrain underlain by permafrost to 38%, 24% and 9% respectively. The morphology of permafrost gain/loss under these scenarios is controlled by the Surface Lapse Rate (SLR), which varies across the region below and above treeline. The SLR is an air temperature elevation gradient that that is noticeably different across the study region. As a result of this attribute three distinct patterns of loss morphology can be identified. Areas that are more maritime exhibit SLRs characteristically similar above and below treeline resulting in low probabilities of permafrost in valley bottoms. Consequently, a loss front moves to upper elevations when warming scenarios are applied (Simple Unidirectional Spatial Loss). Areas where SLRs are gentle below treeline (but normal/negative) and normal above treeline show lower permafrost probabilities with a loss front moving up mountain according to two separate SLRs (Complex Unidirectional Spatial Loss). Finally areas that display high continentally exhibit Bidirectional Spatial Loss where the loss front of lower permafrost probabilities moves up mountain above treeline and down mountain below treeline. Areas that are most affected by permafrost loss are zones with SLRs close to 0 K km−1 where permafrost is extensive, whereas the least susceptible areas to changes in MAAT are above treeline and are highly elevation dependent.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".