Modelling enhanced infiltration of snowmelt ions into frozen soil
Bibliographic record
Abstract
Abstract A model is proposed in which the cumulative load of an ion infiltrating into frozen unsaturated soil can be estimated as a function of meltwater ion concentration and infiltration rate. Assumptions of the model are that the meltwater solution released to the soil surface is conservative, fully mixed within each time step, and that mass and energy are conserved. Infiltration and meltwater concentration are estimated using relationships developed by Gray and Stein respectively. The model suggests that the relationship between ion concentration and volume of infiltration is non‐linear with a positive covariance. Infiltration of snowmelt ions is therefore a function of the products of the mean concentration in the meltwater and the cumulative volume of water that infiltrates, plus the covariance between instantaneous values of ion concentration and infiltration rate. This covariance effect is termed enhanced infiltration . Meteorological observations and soil parameters from four sites in western Canada were used to assess the sensitivity of the model to conditions at a prairie site, a boreal forest site, a mountain forest site, and a shrub tundra site. Model results showed the greatest cumulative infiltration of ion load for the Prairie site; the general ranking was Prairie > Mountain Forest > Boreal Forest > Tundra. However, the greatest impact of enhanced infiltration was found for the Tundra site. At this site, enhanced infiltration caused up to 50% more ion load to infiltrate within the initial third of the melt period compared to infiltration estimates not accounting for this effect. Over the whole melt period, enhanced infiltration caused 55–160% more ion load to infiltrate than estimates based solely on the mean depth of infiltration and ion concentration. Sensitivity analysis showed that enhanced infiltration varies most strongly with initial snow water equivalent, average melt rate over the whole melt period, and snowpack ion elution concentration factor (CF). Copyright © 2007 John Wiley & Sons, Ltd.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.001 | 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 teacher head, 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".