Influence of soil texture on snowmelt infiltration into frozen soils
Bibliographic record
Abstract
This paper describes the influence of soil texture on snowmelt infiltration into frozen soils. Field data collected on frozen, unsaturated agricultural soils of the Canadian Prairies during snow ablation demonstrate: (a) poor association between the amount of infiltration of meltwater released by the seasonal snowcover and soil texture, and (b) small differences in cumulative amounts among soils of widely different textures. A physics-based numerical simulation of heat and mass transfers with phase changes in frozen soils is used to study the mechanics of the infiltration process in representative clay, silty clay loam, silt loam and sandy loam soils. The results of the simulations show that the differences among cumulative snowmelt infiltration into clay, silty clay loam and silt loam soils after 24 h of continuous infiltration are small. Infiltration into a lighter-textured sandy loam after 24 h was on average 23% higher than in the other three soils with most of the increase occurring in the first 5 h of the simulation. Key Words: Soil texture, snowmelt, infiltration, frozen soils
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".