Reply to: “Comment on ‘Stratospheric Aerosol Composition Observed by the Atmospheric Chemistry Experiment Following the 2019 Raikoke Eruption’ by Boone et al.” by Ansmann et al.
Bibliographic record
Abstract
Abstract The question of stratospheric aerosol type following the Raikoke eruption is revisited. Raman lidar measurements suggest the aerosols are predominately smoke, while Atmospheric Chemistry Experiment Fourier transform spectrometer (ACE‐FTS) results indicate the aerosols are predominately sulfate aerosols. The suggested mechanism of smoke particles self‐lofting into the stratosphere is inconsistent with observations in 2020, when more severe Siberian fires failed to invoke a response even vaguely similar to 2019. A side‐by side comparison of the Sarychev and Raikoke eruptions invalidates model calculations that suggest sulfate aerosols should be at levels too low to explain the observed aerosol loading. Structure in infrared absorption spectra provides conclusive evidence of composition, a unique fingerprint for identifying aerosol type. Such information cannot be misinterpreted so long as there is sufficient resolution and spectral coverage. ACE‐FTS infrared aerosol spectra often have an order of magnitude stronger absorption than that of background sulfate aerosols. These spectra can be accurately reproduced by laboratory measured sulfate aerosol spectroscopic information, providing unambiguous identification of the aerosols as sulfate. Visual inspection of thousands of infrared aerosol spectra from the period following the Raikoke eruption indicates the aerosols in the lower stratosphere are predominately sulfate, with no indication of smoke. The lidar study's identification of the aerosols as smoke was based primarily on observed lidar ratios that were more consistent with a material that absorbed significantly at the lidar wavelengths, inconsistent with expectations for sulfate aerosols. However, this could indicate the presence of a substance dissolved in the sulfate aerosols absorbing at those wavelengths rather than smoke particles.
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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 | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.048 | 0.038 |
| Insufficient payload (model declined to judge) | 0.007 | 0.010 |
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".