Examining the influence of microclimate conditions on the breakup of surface‐based temperature inversions in two proximal but dissimilar Yukon valleys
Bibliographic record
Abstract
Abstract Surface‐based temperature inversions (SBIs) are frequent and strong in valleys of north‐central Yukon, which drive annual average surface lapse rates that are strongly inverted (≤1.19°C 100 m −1 ) within the first 100 to 150 vertical meters. This study aims to test the relationship of SBI breakup with local microclimate factors determining the influence on surface lapse rate breakup patterns. A field study was conducted using elevational transect analysis. SBI breakup in the non‐winter season had a strong relationship between the diurnal cycle of increased solar radiation, warming of the surface, and development of atmospheric turbulence in the form of increased wind speed. SBIs during these non‐winter seasons had a peak breakup time of 4 to 8 h following sunrise. Wintertime SBIs often persisted past the diurnal cycle and broke up independent of solar radiation, contributing significantly to the inverted average surface lapse rates. Mechanisms of SBI breakup during the winter are likely more synoptic in nature and one possible mechanism could be shallow cold air flows. Current classification of SBI duration as persistent or transient was reviewed and adjusted, and SBIs with a duration of 18 to 22 h were removed due to the breakup of those events being related to the diurnal cycle.
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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 | 0.001 |
| 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) | 0.000 | 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".