Concentrations, composition, and sources of ice-nucleating particles in the Canadian High Arctic during spring 2016
Bibliographic record
Abstract
Modelling studies suggest that the climate and the hydrological cycle are sensitive to the concentrations of ice-nucleating particles (INPs). However, the concentrations, composition, and sources of INPs in the atmosphere remain uncertain. Here, we report daily concentrations of INPs in the immersion freezing mode and tracers of mineral dust (Al, Fe, Ti, and Mn), sea spray aerosol (Na + and Cl − ), and anthropogenic aerosol (Zn, Pb, NO3-, NH4+, and non-sea-salt SO42-) at Alert, Canada, during a 3-week campaign in March 2016. In total, 16 daily measurements of INPs are reported. The average INP concentrations measured in the immersion freezing mode were 0.005±0.002, 0.020±0.004, and 0.186±0.040 L −1 at −15, −20, and −25 ∘ C, respectively. These concentrations are within the range of concentrations measured previously in the Arctic at ground level or sea level. Mineral dust tracers all correlated with INPs at −25 ∘ C (correlation coefficient, R , ranged from 0.70 to 0.76), suggesting that mineral dust was a major contributor to the INP population at −25 ∘ C. Particle dispersion modelling suggests that the source of the mineral dust may have been long-range transport from the Gobi Desert. Sea spray tracers were anti-correlated with INPs at −25 ∘ C (R=-0.56). In addition, INP concentrations at −25 ∘ C divided by mass concentrations of aluminum were anti-correlated with sea spray tracers (R=-0.51 and −0.55 for Na + and Cl − , respectively), suggesting that the components of sea spray aerosol suppressed the ice-nucleating ability of mineral dust in the immersion freezing mode. Correlations between INPs and anthropogenic aerosol tracers were not statistically significant. These results will improve our understanding of INPs in the Arctic during spring.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".