Software to analyze the relationship between aerosol, clouds, and precipitation: SAMAC
Bibliographic record
Abstract
The analysis of aircraft-based measurements of clouds is critical for studies of aerosol and of clouds. Many such measurements have been taken, but it is difficult to compare such data across instruments, flights and campaigns. We present a new open-source software program, SAMAC (Software for Airborne Measurements of Aerosol and Clouds), that may enable a more systematic and comparable approach to the analysis of aerosol–cloud–precipitation data. The software offers a cooperative and reproducible approach to the analysis of aircraft measurements of clouds across campaigns. SAMAC is an object-oriented software program in which a cloud is an object; all the data related to a cloud is contained in the cloud object. The cloud objects come with built-in methods and functions that allow for the quick generation of basic plots and calculations, SAMAC provides a quick view of the data set and may be used to compare clouds and to filter for specific characteristics. Other researchers can readily use already submitted algorithms once their data is placed in the cloud structure provided, and they can contribute their own algorithms to the software for others to see and use. This approach would improve comparability, reproducibility and transparency by allowing others to replicate results and test the same algorithms on different data. SAMAC can be downloaded at https://github.com/StephGagne/SAMAC/releases.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.036 | 0.017 |
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".