Closure between measured and modeled cloud condensation nuclei (CCN) using size-resolved aerosol compositions in downtown Toronto
Bibliographic record
Abstract
Abstract. Measurements of cloud condensation nuclei (CCN) were made in downtown Toronto during August and September, 2003. CCN measurements were performed at 0.58% supersaturation using a thermal-gradient diffusion chamber, whereas the aerosol size distribution and composition were simultaneously measured with a TSI SMPS and APS system and an Aerodyne Aerosol Mass Spectrometer (AMS), respectively. Aerosol composition data shows that the particles were predominately organic in nature, in particular for those with a vacuum aerodynamic diameter of <0.25 µm. In this study, the largest contribution to CCN concentrations came from this size range, suggesting that the CCN are also organic-rich. Using the size and composition information, detailed CCN closure analyses were performed. In the first analysis, the particles were assumed to be internally mixed, the organic fraction was assumed to be insoluble, and the inorganic fraction was assumed to be ammonium sulfate. The AMS time-of-flight data were used for Köhler theory predictions for each particle size and composition to obtain the dry diameter required for activation. By so doing, this closure analysis yielded an average value of CCNpredicted/CCNobserved=1.12±0.05. However, several sample days showed distinct bimodal distributions, and a closure analysis was performed after decoupling the two particle modes. This analysis yielded an average value of CCNpredicted/CCNobserved=1.03±0.05. A sensitivity analysis was also performed to determine the aerosol/CCN closure if the organic solubility, droplet surface tension, or chamber supersaturation were varied.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".