Bibliographic record
Abstract
Abstract. The Global Space-based Stratospheric Aerosol Climatology (GloSSAC) is essential for understanding and modeling the climatic impacts of stratospheric aerosols. It primarily relies on data from the Stratospheric Aerosol Gas Experiment (SAGE) satellite series, supplemented by the Optical Spectrograph and Infrared Imaging System (OSIRIS) and the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). GloSSAC currently provides stratospheric aerosol extinction coefficients and aerosol optical depths at 525 and 1020 nm. With CALIPSO decommissioned and OSIRIS nearing the end of its operational life, SAGE III/ISS will soon become the sole data source for GloSSAC, but it will only be available as long as the International Space Station (ISS) is operational, around 2030. Therefore, incorporating other measurements, such as those from the Ozone Mapping and Profiler Suite limb profiler (OMPS-LP), is critical. OMPS-LP has provided continuous aerosol extinction coefficient measurements since 2012 with a retrieval algorithm developed by NASA (OMPS(NASA)). However, OMPS(NASA) has been shown to overestimate aerosol extinction coefficients, particularly after the 2022 Hunga Tonga eruption, compared to the tomographic retrieval of OMPS developed by the University of Saskatchewan (OMPS(SASK)) and SAGE III/ISS. Our analysis shows that OMPS(NASA) indeed exhibits a consistently high bias (>50 %) following large volcanic eruptions and pyrocumulonimbus plumes from intense wildfires, while OMPS(SASK) shows reasonable agreement with SAGE III/ISS between 40° S and 40° N. This overestimation by OMPS(NASA) leads to an overestimation of the aerosol effective radiative forcing (ERF) and the associated model-simulated global surface temperature response by about a factor of two.
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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.022 | 0.018 |
| Insufficient payload (model declined to judge) | 0.226 | 0.181 |
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".