The Role of Land Cover Classes and Rainfall Events on the Active Layer Thermal Regime in the High Arctic
Bibliographic record
Abstract
The active layer’s thermal regime, which includes surface energy exchanges and soil thawing/freezing characteristics, is sensitive to environmental factors and processes, and has important implications on biogeochemical, hydrological and geomorphic processes. High Arctic land cover classes were hypothesized to affect the active layer’s thermal regime because they represent variations in environmental factors (e.g., vegetation cover, snow cover and soil moisture content). The objectives of this research were to fill knowledge gaps on the: 1) relationship between land cover classes and active layer thermal regimes, and on the 2) role of rainfall on active layer temperatures in the High Arctic. To address these objectives, air temperature, precipitation, soil temperature and soil moisture data were recorded within three land cover classes (polar semi-desert, mesic tundra and wet sedge) at the Cape Bounty Arctic Watershed Observatory (CBAWO), Nunavut, between 2012 and 2019. The data were used to calculate surface energy exchange (n-factors, surface offsets), mean annual ground temperature at 15 cm depth (MAGT15cm), mean annual ground temperature at the top of permafrost (TTOP), and empirical and modelled maximum thaw depth (MTD). The data were also used to determine characteristics of soil thawing and freezing, ground ice formation and melt, and the active layer’s thermal responses to rainfall. Results showed significant (p < 0.05) interclass differences in n-factors between all three land cover classes. However, MAGT15cm, TTOP and MTD were not significantly different (p > 0.05) between land cover classes (excluding wet sedge where MTD was not calculated), and the empirical MTD was consistently shallower than the modelled MTD. The soil temperature responses to rainfall were consistent between land cover classes and mostly consisted of a dampening of peak diel soil temperatures and a convergence of temperatures with depth. This study was unsuccessful at identifying ice formation and melt within the active layer by purely thermal measures. These results improve our understanding of the relationships between High Arctic land cover classes, rainfall events and the active layer thermal regime, which is critical for our ability to accurately model and predict hydrological and biogeochemical processes, permafrost degradation and landscape stability in continuous permafrost regions.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".