Cloud microphysical characteristics versus temperature for three Canadian field projects
Bibliographic record
Abstract
Abstract. The purpose of this study is to better understand how cloud microphysical characteristics such as liquid water content (LWC) and droplet number concentration (Nd) change with temperature (T). The in situ observations were collected during three research projects including: the Radiation, Aerosol, and Cloud Experiment (RACE) which took place over the Bay of Fundy and Central Ontario during August 1995, the First International Regional Arctic Cloud Experiment (FIRE.ACE) which took place in the Arctic Ocean during April 1998, and the Alliance Icing Research Study (AIRS) which took place in the Ontario region during the winter of 1999–2000. The RACE, FIRE.ACE, and AIRS projects represent summer mid-latitude clouds, Arctic clouds, and mid-latitude winter clouds, respectively. A LWC threshold of 0.005 g m-3 was used for this study. Similar to other studies, LWC was observed to decrease with decreasing T. The LWC-T relationship was similar for all projects, although the range of T conditions for each project was substantially different, and the variability of LWC within each project was considerable. Nd also decreased with decreasing T, and a parameterization for Nd versus T is suggested that may be useful for modeling studies.Key words. Atmospheric composition and structure (cloud physics and chemistry) – Meteorology and atmospheric dynamics (climatology; general circulation)
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".