Laboratory evidence for enhanced infiltration of ion load during snowmelt
Bibliographic record
Abstract
Abstract. Meltwater ion concentration and infiltration rate into frozen soil both decline rapidly as snowmelt progresses. Their temporal association is highly non-linear and a covariance term must be added in order to use time-averaged values of snowmelt ion concentration and infiltration rate to calculate chemical infiltration. The covariance is labelled enhanced ion infiltration and represents the additional ion load that infiltrates due to the timing of high meltwater concentration and infiltration rate. Previous assessment of the impact of enhanced ion infiltration has been theoretical; thus, experiments were carried out to examine whether enhanced infiltration can be recognized in controlled laboratory settings and to what extent its magnitude varies with soil moisture. Three experiments were carried out: dry soil conditions, unsaturated soil conditions, and saturated soil conditions. Chloride solutions were added to the surface of frozen soil columns; the concentration decreased exponentially over time to simulate snow meltwater. Infiltration excess water was collected and its chloride concentration and volume determined. Ion load infiltrating the frozen soil was specified by mass conservation. Results showed that infiltrating ion load increased with decreasing soil moisture as expected; however, the impact of enhanced ion infiltration increased considerably with increasing soil moisture. Enhanced infiltration caused 2.5 times more ion load to infiltrate during saturated conditions than that estimated using time-averaged ion concentrations and infiltration rates alone. For unsaturated conditions, enhanced ion infiltration was reduced to 1.45 and for dry soils to 1.3. Reduction in infiltration excess ion load due to enhanced infiltration increased slightly (2–5%) over time, being greatest for the dry soil (45%) and least for the saturated soil (6%). The importance of timing between high ion concentrations and high infiltration rates was best illustrated in the unsaturated experiment, which showed large inter-column variation in enhanced ion infiltration due to variation in this temporal covariance.
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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) | 0.001 | 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".