BBR and ACM-RT inputs for figures in ACMB-DF documenting paper (Barker et al., 2024).
Bibliographic record
Abstract
For the Earth Cloud, Aerosol, Radiation Explorer (EarthCARE) satellite mission there are a number of algorithms used to process the observations. One of these algorithms, called ACMB-DF, is designed to perform continuous radiative closure assessment of EarthCARE observations. This is done using radiance and flux measurements from the Broad-Band Radiometer (BBR) and forward solar and thermal radiative transfer calculations applied to retrieved geophysical properties. The ACMB-DF algorithm is documented in an Atmospheric Measurements and Techniques (AMT) article. To illustrate the methodology used for ACMB-DF, detailed calculations were performed for the “Hawaii” scene. These include detailed 3D Monte Carlo calculations of the BBR and Multi-Spectral Imager (MSI) radiances applied to select sections of the Hawaii test scene. These where then used in the chain of EarthCARE retrievals to produce geophysical retrievals used as input for the ACM-RT processor to compute radiative quantities needed for closure assessment and as input to generate BBR radiances and fluxes. The relevant publication for these calculations is: Barker, H. W., J. N. S. Cole, N. Villefranque, Z. Qu, A. Velazquez-Blazquez, C. Domenech, S. L. Mason, and R. J. Hogan : Radiative Closure Assessment of Retrieved Cloud and Aerosol Properties for the EarthCARE Mission: The ACMB-DF Product. Submitted to AMT, May 2024.
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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.758 | 0.564 |
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".