The Importance of Biological Particles to the IceNucleating Particle Concentration in a CoastalTropical Site
Bibliographic record
Abstract
Abstract. Atmospheric aerosol particles that can nucleate ice are referred to as ice nucleating particles (INP). Recent studies have confirmed that aerosol particles emitted by mid- and high-latitude 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 the oceans as a source of INPs. Very few studies to 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 topical 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 similar latitudes. Aerosol particles sampled in Sisal are shown to be very efficient INPs, with onset freezing temperatures as high as −3 °C (in some cases), similar to the onset temperature for Pseudomonas syringae. The results show that the INP concentration in Sisal is higher than at other locations sampled with the same type of INP counter. Air masses arriving in Sisal during the passage of cold fronts have, surprisingly, higher INP concentrations than the campaign-average, despite their lower total aerosol concentration. Biological particles were likely found to be very important in ice cloud formation at this tropical location, given the large concentration of INPs above −12 °C. A variety of bacteria and fungi were identified. Although the majority are of terrestrial origin, some of them are clearly oceanic.
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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 | 0.001 | 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".