Biogenic Sources of Ice Nucleating Particles at the High Arctic Site Villum Research Station
Bibliographic record
Abstract
The radiative balance in the Arctic region is sensitive to in-cloud processes, which principally depend on atmospheric aerosols, including ice nucleating particles (INPs). High temperature INPs (active at ≥−15 °C) are common in the Arctic. While laboratory and limited in situ studies show that the high-temperature active INPs are associated with bioaerosols and biogenic compounds, there is still little quantitative insight into the Arctic biogenic INPs and bioaerosols. We measured concentrations of bioaerosols, bacteria, and biogenic INPs at the Villum Research Station (VRS, Station Nord) in a large number of snow (15) and air (51) samples. We found that INPs active at high subzero temperatures were present both in spring and summer. Air INP concentrations were higher in summer (18 INP m –3 at ≥−10 °C) than in spring (<4 INP m –3 at ≥−10 °C), when abundant INPs were found in snowfall (1.4 INP mL –1 at ≥−10 °C). Also, in summer, a significantly higher number of microbial and bacterial cells were present compared to the spring. A large proportion (60%–100%) of INPs that were active between −6 °C and −20 °C could be deactivated by heating to 100 °C, which was indicative of their predominantly proteinaceous origin. In addition, there was a significant linear regression between the summer air concentrations of INPs active at ≥−10 °C and air concentrations of bacterial-marker-genes ( p < 0.0001, R 2 = 0.999, n = 6), pointing at bacterial cells as the source of high-temperature active INPs. In conclusion, the majority of INPs was of proteinaceous, and possibly of bacterial, origin and was found in air during summer and in snowfall during springtime.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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".