Relative importance of acid coating on ice nuclei in the deposition and contact modes for wintertime Arctic clouds and radiation
Bibliographic record
Abstract
Aerosols emitted from volcanic activities and polluted mid-latitudes regions are efficiently transported over the Arctic during winter by the large-scale atmospheric circulation. These aerosols are highly acidic. The acid coating on ice nuclei, which are present among these aerosols, alters their ability to nucleate ice crystals. In this research, the effect of acid coating on deposition and contact ice nuclei on the Arctic cloud and radiation is evaluated for January 2007 using a regional climate model. Results show that the suppression of contact freezing by acid coating on ice nuclei leads to small changes of the cloud microstructure and has no significant effect on the cloud radiative forcing (CRF) at the top of the atmosphere when compared with the effect of the alteration of deposition ice nucleation by acid coating on deposition ice nuclei. There is a negative feedback by which the suppression of contact freezing leads to an increase of the ice crystal nucleation rate by deposition ice nucleation. As a result, the suppression of contact freezing leads to an increase of the cloud ice crystal concentration. Changes in the cloud liquid and ice water contents remain small and the CRF is not significantly modified. The alteration of deposition ice nucleation by acid coating on ice nuclei is dominant over the alteration of contact freezing.
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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 | 0.001 |
| 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) | 0.001 | 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".