Weather systems occurring over Fort Simpson, Northwest Territories, Canada, during three seasons of 1998–1999: 1. Cloud features
Bibliographic record
Abstract
An investigation of high‐latitude continental cloud systems was carried out in the interior of the Northwest Territories of Canada during three multiweek periods during the fall, winter, and spring of 1998–1999 as part of the Canadian Global Energy and Water Cycle Experiment (GEWEX) Enhanced Study. Radar data supplemented by satellite, upper air, and surface observations were used to determine the seasonal behavior of cloud macroscopic properties and compare these with similar observations elsewhere. Unique features included the prevalence of multilayered systems, the cold temperatures of low clouds, and a significant diurnal trend in cloud properties in the winter. A synoptic classification was developed and shown to be an important factor in explaining the variability of cloud properties. A consistent picture emerges of the upslope component and wind shear aloft contributing to the cloud structure in five synoptic classes. Vertically resolved cloud properties highlighted the importance of the ice process in these cloud systems. The cloud system reflectivity and temperature dependencies further supported the synoptic characterizations and highlighted the significance of using seasonally based relationships in automated cloud identification algorithms. The implication of the cloud system variability for radiation measurements was also shown. The radar reflectivity data, degraded to match CloudSat resolution and sensitivity, showed that cloud detection was reliable but that there was a positive bias with cloud thickness. Negative biases in cloud top retrievals based on advanced very high resolution radiometer data were also identified. The Global Environmental Multiscale model illustrated some degree of bias in the occurrence and vertical distribution of these cloud systems. Winter situations in general and midclouds situations in particular were the most poorly handled in both the satellite applications and the model simulations.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".