Development and Applications of ARM Millimeter-Wavelength Cloud Radars
Bibliographic record
Abstract
As the ARM Program was getting underway in the early 1990s, studies by Ramanathan et al. (1989) and Cess et al. (1990) highlighted the importance of cloud and radiation interactions to climate. Ramanathan et al. (1989) demonstrated that, on average, clouds cool the climate system but that different cloud types can have different influences upon it. Cess et al. (1990) showed that general circulation models have an array of different responses to the same sea surface temperature change that result from differences in model clouds and their interactions with radiation. In their papers discussing the ARM Program, Stokes and Schwartz (1994) and later Ackerman and Stokes (2003) emphasized the importance of characterizing clouds throughout a vertical column in order to fully understand the radiation field associated with them. They made clear that coupling of high-fidelity observations of clouds and radiation were necessary to improving model parameterizations of them, which were in turn necessary for improving prognostic models of future climate.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".