A method for separating and quantifying organic and inorganic ice nucleating substances in atmospheric samples based on density gradient centrifugation
Bibliographic record
Abstract
Ice nucleating substances (INSs) influence the properties and frequencies of ice and mixed-phase clouds in the atmosphere, and hence, climate and the hydrological cycle. INSs can be classified as inorganic (e.g., mineral dust, volcanic ash) or organic (e.g., bacterial cells, cell-free proteins). While the properties of both INS classes have been studied in the laboratory, the amounts in the atmosphere are still poorly constrained. Here, we demonstrate a new method for separating and quantifying inorganic and organic INSs. First, INS suspensions were separated into a high-density isolate containing inorganic INSs and a low-density isolate containing organic INSs using density gradient centrifugation, and then INSs were quantified in each isolate using a droplet freezing technique. Inorganic K-feldspar and organic Snomax INSs were used to test our method. The average K-feldspar INS recovery in the high-density isolate was 54%, with no evidence of K-feldspar INSs in the low-density isolate. The average Snomax INS recovery in the low-density isolate was 27%, with small amounts of Snomax contaminating the high-density isolate. A mixture of K-feldspar and Snomax was successfully separated, with recoveries comparable to those observed for K-feldspar and Snomax individually. Recoveries less than 100% can be explained by losses of INSs to vessel walls, accidental mixing of the different density layers during pipetting, and incomplete collection of material during pipetting.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".