Shallow soil moisture – ground thaw interactions and controls – Part 1: Spatiotemporal patterns and correlations over a subarctic landscape
Bibliographic record
Abstract
Abstract. Soil moisture and ground thaw state are both indicative of a hillslope's ability to transfer water. In cold regions, in particular, it is widely known that the depth of the active layer and wetness of surface soils are important for runoff generation, but the diversity of interactions between ground thaw and surface soil moisture themselves has not been studied. To fill this knowledge gap, detailed shallow soil moisture and thaw depth surveys were conducted along systematic grids at the Baker Creek Basin, Northwest Territories. Multiple hillslopes were studied to determine how the interactions differed along a spectrum of topological, typological and topographic situations across the landscape. Overall results did not show a simple link between soil moisture and ground thaw as was expected. Instead, correlation was a function of wetness. The interaction between soil moisture and ground thaw was more dependent at wetter sites. This indicates that interactive soil moisture and thaw depth behaviour on hillslopes in cold regions changes with location and cannot necessarily be lumped together in hydrological models. To explore further why these differences arise, a companion paper (Guan et al., 2010) will examine how the hydrological and energy fluxes influenced the patterns of moisture and thaw among the study sites.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".