Bibliographic record
Abstract
Abstract Aerosol size is still a poorly constrained quantity in general circulation models (GCMs). By using the modal radii of the coarse and fine mode retrieved from 103 stations in the Aerosol Robotic Network (AERONET) and the fine mode aerosol optical depth fraction derived from both the Moderate Resolute Imaging Spectroradiometer (MODIS) Terra and AERONET, a globally and monthly averaged aerosol size distribution dataset was computed assuming internally mixed aerosols. Different methods were employed in creating the size distribution datasets that were input to the ECHAM4 climate model giving a globally averaged aerosol optical depth (AOD) at 500 nm that ranged from 0.11 to 0.20 depending on the method. This translates into a globally averaged direct aerosol top-of-atmosphere forcing range from −1.6 to −3.9 W m−2. Reducing the uncertainty in the aerosol sizes is important when using AOD to validate models since mass burden errors can then be assumed to be the main AOD error source. This paper explores a procedure that can help achieve this goal.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".