Ion enrichment of snowmelt runoff water caused by basal ice formation
Bibliographic record
Abstract
Abstract Once meltwater reaches the base of a snowpack it can infiltrate the underlying stratum, runoff, or refreeze and form a basal ice layer. Basal ice formation is most common early in melt over saturated or very cold frozen soils. Initial meltwater becomes enriched in ion concentrations compared to the parent snow due to ion fractionation during thaw and percolation through the snowpack. If ion exclusion occurs during basal ice formation, further enrichment of initial runoff water ion concentrations might occur. The influence of basal ice formation on runoff water chemistry was examined by comparing ion concentrations in runoff water that had sustained basal ice contact, to meltwater before basal ice contact. A series of experiments, involving melting a snowpack in a large insulated box over a cold impermeable substrate in a temperature‐controlled room, were carried out. A cooling system at the chemically inert base ensured formation of basal ice during snowmelt. Meltwater samples were collected throughout melt from within the snowpack using an extraction tube; runoff water was collected at the base. All samples were analysed for major anions and cations. Results showed that formation of basal ice layers can sometimes enrich the initial runoff water compared to meltwater before basal ice contact. Ion concentrations in basal ice contact runoff water were up to sixteen times greater than those in no‐contact meltwater; however, on average, basal ice contact runoff water showed 1·5 times the ion concentrations of the no‐contact meltwater. Enrichment was greatest with the rapid formation of a thick basal ice layer. The implications are that basal ice formation alters both meltwater ion pathway and concentration. When no basal ice is present, enhanced infiltration of meltwater ion load can cause relatively dilute runoff water. When basal ice is present all meltwater runs off and further ion‐concentration enrichment occurs. Copyright © 2008 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) | 0.002 | 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".