Distinction of Supercooled droplets from the Ice Crystals in Mixed-phase Cloud Regime: McGill Real-time Ice Nucleation Chamber (MRINC)
Bibliographic record
Abstract
<!--!introduction!--> Atmospheric ice-nucleating particles (INP) play an essential role in determining the optical thickness, lifetime, and phase of clouds (mixed-phase and cirrus clouds) [P. J. DeMott et al., 2010]. These characteristics of clouds, in turn, impact the Earth’s radiative budget. Despite significant advancements in the fundamental understanding of different ice formation processes in the last decades, the ice phase in clouds still contributes to substantial uncertainty in climate model predictions of the radiative forcing [Paul J. DeMott et al., 2011]. This presentation introduces the newly developed portable McGill Real-time Ice Nucleation Chamber (MRINC) for studying ice nucleation processes of nano to micron-sized particles in situ in real-time. The MRINC allows measuring INP concentrations under conditions pertinent to mixed-phase cloud temperatures from about −10 °C to about −38 °C. The MRINC is coupled with aerosol sizers (6 nm to 10 µm) and a Nano-Digital In-line Holographic Microscope (Nano-DIHM) to record the size distribution, phase, and shape of INPs. The characterization includes determining aerosol particles' size, shape, morphology, phase, and surface properties. We have shown preliminary results as proof of concept, where Nano-DIHM coupled with MRINC successfully distinguished silver iodide nucleated ice crystals and supercooled droplets in real time. We also provide an example of real-time capturing of the growth of sodium chloride (NaCl) and ammonium sulphate ((NH4)2SO4) aerosol particles in controlled temperature and humidity conditions using MRINC. We demonstrate that MRINC could be used for cloud condensation and ice nucleation studies.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".