Comparison of Snowmelt Infiltration under Different Soil‐Freezing Conditions Influenced by Snow Cover
Bibliographic record
Abstract
The depth of soil frost is decreasing in cold regions around the world as a result of climate warming. To evaluate the potential impacts of the reduction in frost depth on the hydrologic cycle, it is necessary to understand snowmelt infiltration processes in frozen soils. A field study was conducted at an agricultural site characterized by volcanic ash soil in Tokachi, Hokkaido, Japan, where frost depths have decreased significantly in the last 20 yr. Soil temperature, water content, matric potential, snow cover, and meteorological parameters were monitored to quantify snowmelt infiltration flux for four winters that had different snow and soil conditions. When snowmelt began, the soil frost was 0.1 to 0.2 m thick in two winters and was absent in two other winters, providing a unique opportunity to compare snowmelt infiltration under frozen and unfrozen conditions. Most of the snowmelt water infiltrated into the soil under both frozen and unfrozen conditions, indicating that the frozen soil layer did not impede infiltration. The lack of flow impedance in the frozen soil was partly due to relatively high air temperature and an absence of freeze‐back events during the snowmelt period. Furthermore, the temperature of the frozen soil layer was close to 0°C when the melt started, meaning that very little meltwater refroze in the soil before the temperature reached 0°C. The thick (>1 m) snow cover insulated the soil surface, allowing the frozen soil layer to warm up with the upward conduction of heat from the unfrozen layer below. These results indicate the importance of the interaction between snow cover and soil, which can be significantly affected by climate change.
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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 | 0.000 |
| 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 | 0.000 | 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".