Supplementary material for "Inverse Modeling of the Initial Stage of the 1991 Pinatubo Volcanic Cloud Accounting for Radiative Feedback of Volcanic ash" paper
Bibliographic record
Abstract
Supplementary material for "Inverse Modeling of the Initial Stage of the 1991 Pinatubo Volcanic Cloud Accounting for Radiative Feedback of Volcanic ash" by A. Ukhov, G. Stenchikov, S.Osipov, N. Krotkov, N. Gorkavyi, C. Li, O. Dubovik, and A. Lopatin. Corresponding author: Alexander Ukhov, alexander.ukhov@kaust.edu.sa Contents 0. This file 'README' 1. Emission profiles (Mt/sec) for ash and SO2 (in pickle and txt format). 1.1 Files 'ash_2d_emission_profiles' and 'ash_2d_emission_profiles.txt' 1.2 Files 'so2_2d_emission_profiles' and 'so2_2d_emission_profiles.txt' 2. python script 'draw_supplementary_profiles.py' plots inverted emission profiles (in pickle format) and their time integrated variants. 3. WRF-Chem output file 'wrfout_d01_1991-06-16_00:00:00' in netcdf format contains 3-D fields of ash, sulfate, and SO2 concentrations at 0000 UTC on 16 of June. Instructions on how to process WRF-Chem output are available at the Appendix of [1]. 4. WRF-Chem domain grid description in the file 'wrf_small_grid.txt'. This file can be used for conservative interpolation of 3-D fields to another grid, for example using 'cdo remapcon'. There are two options: 1. Use inverted ash and SO2 emission profiles (see p.1 and p.2) 2. Use ash, sulfate, and SO2 concentrations from WRF-Chem output file (see p.3 and p.4) as initial conditions for another run. References: 1. Ukhov, A., Ahmadov, R., Grell, G., and Stenchikov, G.: Improving dust simulations in WRF-Chem v4.1.3 coupled with the GOCART aerosol module, Geosci. Model Dev., 14, 473–493, https://doi.org/10.5194/gmd-14-473-2021, 2021.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.672 | 0.183 |
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".