Ice-nucleating particles in a coastal tropical site
Bibliographic record
Abstract
Atmospheric aerosol particles that can nucleate ice are referred to as ice-nucleating particles (INPs). Recent studies have confirmed that aerosol particles emitted by the oceans can act as INPs. This very relevant information can be included in climate and weather models to predict the formation of ice in clouds, given that most of them do not consider oceans as a source of INPs. Very few studies that sample INPs have been carried out in tropical latitudes, and there is a need to evaluate their availability to understand the potential role that marine aerosol may play in the hydrological cycle of tropical regions. This study presents results from the first measurements obtained during a field campaign conducted in the tropical village of Sisal, located on the coast of the Gulf of Mexico of the Yucatan Peninsula in Mexico in January–February 2017, and one of the few data sets currently available at such latitudes (i.e., 21 ∘ N). Aerosol particles sampled in Sisal are shown to be very efficient INPs in the immersion freezing mode, with onset freezing temperatures in some cases as high as −3 ∘ C, similarly to the onset temperature from Pseudomonas syringae . The results show that the INP concentration in Sisal was higher than at other locations sampled with the same type of INP counter. Air masses arriving in Sisal after the passage of cold fronts have surprisingly higher INP concentrations than the campaign average, despite their lower total aerosol concentration. The high concentrations of INPs at warmer ice nucleation temperatures (T>-15 ∘ C) and the supermicron size of the INPs suggest that biological particles may have been a significant contributor to the INP population in Sisal during this study. However, our observations also suggest that at temperatures ranging between −20 and −30 ∘ C mineral dust particles are the likely source of the measured INPs.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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".