Emerging investigator series: spatial distribution of dissolved organic matter in ice and at air–ice interfaces
Bibliographic record
Abstract
Dissolved organic matter (DOM) is a common solute in snow and ice at Earth's surface. Its effects on reaction kinetics in ice and at air-ice interfaces can be large, but are currently difficult to quantify. We used Raman microscopy to characterize the surface and bulk of frozen aqueous solutions containing humic acid, sodium dodecyl sulfate (SDS), and citric acid at a range of concentrations and temperatures. The surface-active species (humic acid and SDS) were distributed differently than citric acid. Humic acid and SDS are almost completely excluded to the air-ice interface during freezing, where they form a film that coats the surface nearly completely. A liquid layer that coats the majority of the surface was observed at all humic acid and SDS concentrations. Citric acid, which is smaller and less surface active, is excluded to liquid channels at the air-ice interface and within the ice bulk, as has previously been reported for ionic solutes such as sodium chloride. Incomplete surface wetting was observed at all citric acid concentrations and at all temperatures (up to -5 °C). Citric acid appears to be solvated in frozen samples, but SDS and humic acid do not. These results will improve our understanding of the effects of organic solutes on environmental and atmospheric chemistry within ice and at air-ice interfaces.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 0.008 |
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".