Meteorological polarimetric phased array radar
Bibliographic record
Abstract
Polarimetric meteorological radars have become crucial for the detection and short-term forecast of hazardous weather, as well as quantitative precipitation estimation (QPE). The ability to directly identify hail and gauge its size, detect tornado debris, and anticipate flash floods sets these radars apart from the classical single-polarized ones. Weather agencies in many countries have deployed and/or upgraded their radar systems to dual polarization, including the US National Weather Service (NWS) which completed the upgrade of its Weather Service Radar-1988 Doppler (WSR-88D) network in 2013. Recent experience with the WSR-88D and other operational radars has exceeded expectations, well justifying the upgrade cost, which was about 5% of the initial procurement. The high quality of the quantitative polarimetric measurements has set a standard for any future polarimetric weather radar.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.494 | 0.002 |
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; both teacher heads agree on what is shown here.
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".