NOAA profiler network and other emerging global profiler networks
Bibliographic record
Abstract
The U.S. NOAA Profiler Network operated by the Forecast Systems Laboratory for more than a decade represents the culmination of several decades of research and development of wind profiling Doppler radars. The NOAA Profiler Network is comprised of 35 tropospheric wind profilers (404/449 MHz) mostly located in the central United States. The infrastructure, built over the years for the NOAA Profiler Network has the flexibility and capacity to handle many other profilers in addition to the 35 NOAA Profiler Network systems. With recent advances in computers, networking and communication technologies, real-time profiler data can be acquired from almost anywhere on the globe. Data from remote sites are submitted to quality control and placed onto the Global Telecommunication System. Currently the Forecast Systems Laboratory is receiving data from about 80 sites in the continental U.S., Alaska, Canada, and along the equator west from South America. The data are routed to operational forecast centers where the data are used in a variety of numerical weather prediction models and also distributed to the local forecast offices to tailor model guidance to local conditions. The data are also placed on the Forecast Systems Laboratory web site http://www.profiler.noaa.gov. Here the data may be viewed in many graphical forms and are also available for downloading to a user’s site in numeric format.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.036 | 0.022 |
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".