Field And Laboratory Study Of Infiltration Processes During Melt Events In Frozen Prairie Soils
Bibliographic record
Abstract
In the northern hemisphere snowmelt infiltration into frozen ground can be dependent on preferential flow along larger pores called macropores which can play a critical role in directing snowmelt for groundwater storage. Areas like the Canadian Prairies can undergo two or more melt events in a year which can change the soil storage capacity and influence how snowmelt is partitioned between infiltration and runoff during spring. The effects of these ‘mid-winter’ melt events on soil pore networks are not well understood, making it difficult to incorporate them in hydrological models. This study investigated the infiltration processes during melt events by performing a series of tracer tests on a cropland and grassland site and a set of infiltration experiments on frozen soil columns. Results from the laboratory study show that macropore flow is the dominant transport mechanism during melt events leading to deep percolation and minimal interaction between infiltrating water and the soil matrix. Snowmelt that infiltrates during mid-winter melt events infiltrate and refreezes in air-filled soil matrix pores first which, along with the heat energy exchanged between the soil matrix and infiltrating water, can result in snowmelt from later melt events to refreeze in macropores as ice “plugs” rather than completely blocking a macropore network. This reduces infiltration in frozen soils during spring melt, encouraging more runoff and ponding. Macropore connectivity can affect infiltration rates as seen with the greater runoff ratios on the cropland site which was less macroporous than the grassland site. It can also influence refreezing dynamics in pores as runoff was not always necessarily higher on croplands during spring melt.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".