Black Carbon Particles Do Not Matter for Immersion Mode Ice Nucleation
Bibliographic record
Abstract
Abstract The role of black carbon (BC) in ice crystal formation via immersion freezing relevant for mixed‐phase cloud formation is uncertain. Previous studies report either negligible or significant contributions of BC particles to cloud glaciation via immersion freezing. Despite conflicting evidence, immersion freezing by BC particles is included in several cloud models. Here we show that fossil fuel soot and commercially available hydrocarbon BC is inactive as immersion freezing nuclei for atmospherically relevant particle sizes and surface areas. Instead, temperatures <235 K are necessary for freezing droplets with immersed soot particles, implying homogeneous freezing, rather than immersion freezing by soot. A comparison of the results to previous studies using larger soot aggregates and dust reveals the ineffectiveness of soot as immersion ice nucleating particles. We conclude that soot particles with properties like those investigated here can be neglected for simulating ice nucleation in mixed‐phase clouds.
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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 | 0.001 |
| 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 | 0.001 | 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 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".