On the availability of uncoated mineral dust ice nuclei in cold cloud regions
Bibliographic record
Abstract
The ice nucleation efficiency of mineral dust decreases when it acquires coatings, e.g. through processing in liquid clouds. This study explores the availability of unprocessed mineral dust for interactions with clouds. We performed forward trajectory calculations originating near the surface of the Chinese Taklimakan desert. The initial specific humidity of each trajectory was assumed to be conserved and used to calculate the relative humidities with respect to water and ice, allowing us to estimate the formation of liquid, mixed‐phase and ice clouds downstream. Practically none of the simulated air parcels reached conditions suitable for homogeneous nucleation of ice (T ≲ −40°C) without experiencing water saturation first. Potentially the biggest impact of mineral dust is predicted to be on mixed‐phase clouds. Furthermore, most trajectories passed through ice‐saturated (but water‐subsaturated) regions where “warm” (T ≳ −40°C) ice clouds may form prior to 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 | 0.000 | 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".