The relationship between surface tension and atmospheric ice-nucleating activity of agricultural soil
Bibliographic record
Abstract
Ice-nucleating particles (or INP) play an important role in controlling cloud radiative properties and lifetimes. Therefore, understanding the sources and mechanisms of ice formation in clouds is vital for understanding their impact on cloud radiative feedback. Agricultural dust contributes 25% of global dust emissions and has been shown to nucleate ice at temperatures up to -6°C. This high nucleating ability of agricultural soils suggests that they may be an essential source of INPs on regional or global scales. Many organic components, which have been shown to be important for ice nucleation in soils, have surface active properties that may enhance the ice-nucleating ability of the soil. In this work, lignin was used as a reference for investigating surfactant macromolecules as a potential component of ice nucleation. Lignin solutions showed high ice-nucleating activity in line with decreases in surface tension. We contrasted our observations of lignin with observations from soil extractions from samples taken in the field. Preliminary results suggest little correlation between surface tension measurements and the ice-nucleating activity of extracted soil samples. The presence of a correlation between ice-nucleating and surface activity in soil components such as lignin, but the absence of this correlation in complete soil samples suggests that surfactants can be important ice-nucleating macromolecules, but that highly active soil samples do not necessarily reduce the surface tension at the water-air interface.
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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".