Ice-nucleating efficiency of aerosol particles and possible sources atthree coastal marine sites
Bibliographic record
Abstract
Abstract. Despite the importance of ice-nucleating particles (INPs) for climate and precipitation, our understanding of these particles is far from complete. Here, we investigated INPs at three coastal marine sites in Canada, two at mid-latitude (Amphitrite Point and Labrador Sea), and one in the Arctic (Lancaster Sound). At all three sites, the ice-nucleating efficiency on a per number basis (expressed as the fraction of aerosol particles acting as an INP) was strongly dependent on the size. For example, at diameters of around 0.2 µm, approximately 1 in 106 particles acted as an INP at −25 ºC, while at diameters of around 8 µm, approximately 1 in 10 particles acted as an INP at −25 ºC. The ice-nucleating efficiency on a per surface area basis (expressed as the surface active site density, ns) was also dependent on the size, with larger particles being more efficient at nucleating ice. The ns values of supermicron particles at Amphitrite Point and Labrador Sea were larger than previously measured ns values of sea spray aerosol, suggesting that sea spray aerosol was not a major contributor to the supermicron INP population at these two sites. Consistent with this observation, a global model of INP concentrations under-predicted the INP concentrations when assuming only marine organics as INPs. On the other hand, assuming only K-feldspar as INPs, the same model was able to reproduce the measurements at a freezing temperature of −25 ºC, but under-predicted INP concentrations at −15 ºC, suggesting that the model is missing a source of INPs active at a freezing temperature of −15 ºC.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".