Equivalent Elevation: A New Method to Incorporate Variable Surface Lapse Rates into Mountain Permafrost Modelling
Bibliographic record
Abstract
ABSTRACT Permafrost is present at multiple elevations with no defined lower limit in the southern Yukon Territory, Canada. Empirical statistical modelling of permafrost probability in the region required the development of equivalent elevation , a new variable that reflects measured differences between surface air temperature lapse rates below and above treeline. In areas where surface lapse rates are negative (normal) but gentle up to the altitudinal treeline, equivalent elevation results in a compressed elevational range. Where surface lapse rates are positive (inverted) in the forest due to the strength of winter inversions, equivalent elevations calculated for valley floors are higher than those at treeline. There is a strong relationship between the magnitude and sign of surface lapse rates below treeline and the annual amplitude of monthly air temperatures at nearby climate stations, which permits prediction of equivalent elevation for the entire region. Permafrost probability modelling using equivalent elevation produced statistically significant results in several study areas whereas actual elevation values did not. The concept is of particular use where forested areas are underlain by permafrost and may be transferable to areas with similar terrain and climate such as those in the Canadian Northwest Territories, Alaska and Mongolia. Copyright © 2011 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".