Surface temperature inversion characteristics in dissimilar valleys, Yukon Canada
Bibliographic record
Abstract
Permafrost distribution in high-latitude continental mountains is a product of both latitudinal and elevationally controlled attributes. Frequently occurring surface-based temperature inversions (SBIs) significantly modify surface lapse rates (SLRs) annually. We aim to identify and quantify patterns of SBI characteristics in two proximal yet morphologically and vegetatively dissimilar central Yukon valleys. Elevational transect analysis is applied by using sensors in valley bottoms and 100 m upslope to determine in situ SLRs for the study period (August 2017 – August 2021). SLRs were shown to vary significantly between these dissimilar valleys. Climate reanalysis products (ClimateNA and Globsim) underestimated or almost entirely missed the presence of strong SBIs which produce annual average SLRs that range from 0.46 to 1.2 °C 100 m −1 . The magnitude of these hyper-inversions was grossly underpredicted by previous surface air temperature modelling that attempted to account for SBIs across Yukon. Our results support the previously conceptualized framework that strong SBIs influence surface air temperatures and the pattern of permafrost distribution.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.006 | 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 teacher head, 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".