The UNAM-MARine Aerosol Tank (UNAM-MARAT): An Evaluation of the Ice-Nucleating Abilities of seawater from the Gulf of Mexico and the Mexican Pacific
Bibliographic record
Abstract
Abstract. Although several studies have shown that sea spray aerosol (SSA) has the potential to act as ice nucleating particles (INP) impacting cloud formation, there is a lack of marine INP studies in tropical latitudes. This is partly due to the unavailability of local oceanographic cruises that perform aerosol-cloud interaction studies in the tropics, as well as the scarcity of appropriate aerosol and cloud microphysics instrumentation. The present study shows the development of the UNAM-MARine Aerosol Tank (UNAM-MARAT), a device that simulates wave breaking to generate SSA particles with the main purpose to characterize their physicochemical properties including their ice nucleating abilities. The UNAM-MARAT was characterized using Instant Ocean Sea Salt and its potential to study ambient sea waters was evaluated with sea seawater samples collected from the Port of Veracruz (PoV) in the Gulf of Mexico, and from the Bay of Acapulco (BoA) and the Bay of Santiago- Manzanillo (BoSM) in the Mexican Pacific Ocean. The portable and automatic UNAM-MARAT is able to generate aerosol particle concentrations as high as 2000 cm-3 covering a wide range of sizes, from 30 nm to 10 μm, similar to those found in the ambient marine boundary layer. The SSA generated from the three natural seawater samples was found to act as INP via immersion freezing, with INP concentrations as high as 130.7 L-1. The particles generated from the BoA seawater samples were the most efficient INPs, reporting the highest ice active site density (ns) values between -20 and -30 °C. Our results also show the direct relationship between particle size and its composition. Larger particles (> 1 μm) were found to be enriched in sodium chloride. In contrast, the fraction of Ca2+, Mg2+, and NO3- was found to increase with decreasing the particle size from 10 μm to 320 nm. This suggests the presence of dissolved organic material in the submicron particles.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| 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 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.003 |
| 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 teacher head, 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".