Results from the University of Toronto continuous flow diffusion chamber (UT-CFDC) at the international workshop for comparing ice nucleation measuring systems (ICIS 2007)
Bibliographic record
Abstract
Abstract. The University of Toronto continuous flow diffusion chamber (UT-CFDC) was used to study heterogeneous ice nucleation at the International Workshop on Comparing Ice Nucleation Measuring Systems (ICIS 2007) which also represented the 4th ice nucleation workshop, on 14–28 September 2007. One goal of the workshop was to inter-compare different ice nucleation measurement techniques using the same aerosol sample source and preparation method. The aerosol samples included four types of desert mineral dust, graphite soot particles, and live and dead bacterial cells (Snomax®). This paper focuses on the UT-CFDC results, with a comparison to techniques of established heritage including the Colorado State CFDC and the AIDA expansion chamber. Good agreement was found between the different instruments with a few specific differences attributed to the variation in how onset of ice formation is defined between the instruments. It was found that when efficiency of ice formation is based on the lowest onset relative humidity, Snomax® particles were most efficient followed by the desert dusts and then soot. For all aerosols, deposition mode freezing was only observed for T < 245 K except for the dead bacteria where freezing occurred below water saturation as warm as 263 K.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.005 | 0.001 |
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".