Factors controlling the ice nucleating abilities of <i>α</i>‐pinene SOA particles
Bibliographic record
Abstract
Abstract The ice nucleation abilities of fresh, water‐soluble, internally mixed, and photochemically oxidized α‐pinene secondary organic aerosol (SOA) particles were investigated at cirrus cloud temperatures in a continuous flow diffusion chamber. SOA sampled from a flow tube (SOA‐fresh‐FT) mimicked freshly generated particles, while the water‐soluble organic compound fraction from a FT and smog chamber (SOA‐WSOC‐FT, SOA‐WSOC‐SC) mimicked cloud‐processed particles. SOA‐fresh‐FT, SOA‐WSOC‐FT, and SOA‐WSOC‐SC particles were not highly active at nucleating ice between 233 K and 213 K, with activation onsets (i.e., 0.1% of particles forming ice) at or slightly above the theoretical homogeneous freezing line. A significant increase in the O/C of SOA‐WSOC‐SC via aqueous phase OH oxidation did not modify the ice nucleation abilities, indicating that the detailed composition of the particles is not of paramount importance to their ice nucleating abilities. Instead, precooling the SOA‐WSOC‐FT and SOA‐WSOC‐SC particles to 233 K dropped their ice nucleation onsets by up to 20% relative humidity with respect to ice, with lower temperatures likely driving the particles to be more viscous and solid‐like. However, it is possible that preactivation contributed to the reduction of the ice nucleation onsets. Particles composed of both SOA‐WSOC and ammonium sulfate (AS) were significantly less active in the deposition nucleation mode than pure, solid AS particles.
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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".