Multiple scattering correction factor estimation for aethalometer aerosol absorption coefficient measurement
Bibliographic record
Abstract
We estimate the multiple scattering correction factor (Cref), which is an empirical constant required to correct aerosol absorption coefficient (σap) measurements for the multiple scattering artifacts of aethalometer, using a multiplier derived from a linear regression method (CrefLRL). Estimated CrefLRL values during the Cheju ABC Plume Monsoon EXperiment (CAPMEX) are 3.99 (405 nm), 4.48 (532 nm), and 5.46 (781 nm) using aethalometer and 3-wavelength PhotoAcoustic Soot Spectrometer (PASS-3). The difference between these CrefLRL values and those of a previous study (CrefW03) are ˗8.0% (405 nm), 20.1% (532 nm), and 30.2% (781 nm); the difference is greater at larger wavelengths because the linear regression line intercept is larger. CrefW03 varies by up to 121% with increasing aerosol absorption coefficient (σap) at 532 and 781 nm, whereas CrefLRL varies by only 36.8%. CrefW03 and CrefLRL determined during CAPMEX were applied to year-round aethalometer σap measurements (σapW03 and σapLRL, respectively) at Gosan (GSN), Lulin (LLN), and Alert (ALT) stations. σapW03 and σapLRL were compared to concurrent σap measurements from Continuous Light Absorption Photometer (CLAP; σapCLAP). At GSN, the bias difference and root mean square difference of σapW03 from σapCLAP are ˗23.1 and 25.8%; however, those of σapLRL from σapCLAP are ˗9.0 and 17.9%, respectively. LLN and ALT both exhibit a greater difference between σapW03 and σapCLAP than between σapLRL and σapCLAP. This suggests that CrefLRLcan be applied to year-round aethalometer measurements. Furthermore, σapLRL agrees better with σapCLAP than σapW03 in all three environments.Copyright © 2019 American Association for Aerosol 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".