Measurement report: Ice nucleating abilities of biomass burning, African dust, and sea spray aerosol particles over the Yucatan Peninsula
Bibliographic record
Abstract
Abstract. Most precipitation from deep clouds over the continents and in the intertropical convergence zone is strongly influenced by the presence of ice crystals, whose formation requires the presence of ice nucleating particles (INP). Although there are a large number of INP sources, the ice nucleating abilities of aerosol particles emitted from oceans, deserts, and wildfires are poorly described at tropical latitudes. To fill this gap in knowledge, the UNAM-MicroOrifice Uniform Deposit Impactor-Droplet Freezing Technique (UNAM-MOUDI-DFT) was built. Aerosol samples were collected in Sisal and Merida, Yucatan (Mexico) under the influence of cold fronts, biomass burning (BB), and African dust (AD), during five short-term field campaigns between January 2017 and July 2018. The three different aerosol types were distinguished by characterizing their physicochemical properties. Marine aerosol (MA), BB, and AD air masses were found to contain INP; the highest concentrations were found for AD (from 0.071 L−1 to 36.07 L−1), followed by MA (from 0.068 L−1 to 18.90 L−1), and BB (from 0.063 L−1 to 10.21 L−1). However, MA had the highest surface active site density (ns) between −15 °C and −30 °C. Additionally, supermicron particles contributed more than 72 % of the total INP concentration independent of aerosol type; MA had the largest contribution from supermicron particles.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".