Ice-nucleating particles in the central Arctic
Bibliographic record
Abstract
A small subset of aerosol particles can induce ice-nucleation in supercooled liquid droplets. These ice-nucleating particles (INP) are responsible for the primary, heterogeneous nucleation of ice in clouds, and knowledge of their concentrations, sources and characteristics is necessary to accurately represent these mixed-phased clouds in models. This is particularly important in regions such as the central Arctic Ocean, where there are persistent mixed-phased clouds that help shape the radiative budget of the Arctic but very few measurements of INP, none of which are at cloud altitude. This thesis aimed to tackle the dearth of central Arctic INP data through the design and use of novel instrumentation, and a field campaign aboard an icebreaker which saw measurements of INP measurements made at both ship and cloud level close to the North Pole (88-90°N). Firstly, a high-volume, size-selective aerosol sampler capable of being deployed for hours at a time at altitudes and temperatures relevant for mixed phased clouds was designed and tested. This sampler was used on a 2-month campaign to the central Arctic ocean from August-September 2018, alongside ship-based INP measurements. The central Arctic INP concentrations at sea-level were highly variable, with concentrations as low as could be expected in the Southern Oceans, and as high as those measured in rural farmland. The INP were found to be heat-sensitive, and the most active samples originated from the Arctic coasts of Russia. The samples with the least INP activity were from the pack ice and Canadian Arctic. The concentrations measured at cloud-level were often decoupled from those at the surface, demonstrating the necessity for more airborne measurements of INP. Additionally, the INP at cloud-level were often smaller than expected, at <0.25 μm in aerodynamic diameter. Finally, in order to better probe the characteristics of sampled INP in the future, a microfluidic device capable of sorting ice crystals containing INP active at a specific temperature from the bulk sample was developed.
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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 | 0.000 | 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".