An alternative method for estimating hygroscopic growth factor of aerosol light-scattering coefficient: a case study in an urban area of Guangzhou, South China
Bibliographic record
Abstract
Abstract. A method was developed to estimate hygroscopic growth factor (f(RH)) of aerosol light-scattering coefficient (bsp), making use of the measured size- and chemically resolved aerosol samples. In this method, chemical composition of the measured aerosol samples were first reconstructed using the equilibrium model ISORROPIA II. The reconstructed chemical composition, which varies with relative humidity (RH), was then employed to calculate bsp and hygroscopic growth factor of bsp (fsp(RH)) using the Mie model. Furthermore, the calculated fsp(RH) was fitted with an empirical curve. To evaluate the applicability of fsp(RH), the curve of fsp(RH) was used to correct the long-term records of the measured bsp from the values under comparative dry conditions to the ones under ambient RH conditions. Compared with the original bsp data, the fsp(RH)-corrected bsp had a higher linear correlation with, and a smaller discrepancy from, the bsp derived directly from visibility and absorption measurements. The fsp(RH) determined here was further compared with that reported in previous studies. The method described in this manuscript provides an alternative approach to derive credible fsp(RH) with high accuracy and has many potential applications in aerosol-related research.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".