Organic Contaminant Release from Melting Snow. 1. Influence of Chemical Partitioning
Bibliographic record
Abstract
A melting snowpack can deliver organic contaminants to terrestrial and aquatic ecosystems in the form of short and concentrated pulses. The mechanisms and kinetics of the underlying processes need to be understood to successfully integrate them into contaminant and water quality models. Controlled laboratory-based snowmelt experiments using artificially produced snow spiked with organic target contaminants reveal how chemical behavior during melting is dependent on the partitioning between the different phases within the bulk snow. Behaving similar to inorganic ions, water soluble organic chemicals, such as atrazine, are preferentially released at an early stage of melting, because such chemicals, accumulated at the snow grain surface, dissolve in the downward percolating meltwaterfront. Hydrophobic substances attached to particles, such as the larger polycyclic aromatic hydrocarbons, are often released at the very end of the melt period, because particle coagulation and snow densification render the melting snowpack an efficient filter trapping the particles. A notable fraction of volatile chemicals, such as naphthalene, will transfer from the melting snowpack to the lower atmosphere due to evaporation. Organic pollutants with intermediate partition properties, such as lindane, can easily switch between the bulk snow phases and their elution behavior is therefore more sensitive to varying snow and melt characteristics.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| 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".