The Evolution and Impact of a Meteorological Outreach Program for Public Safety Officials: An Update on the Oklahoma Mesonet’s OK-First Program
Bibliographic record
Abstract
Abstract Since 1997, the Oklahoma Mesonet (the state’s automated mesoscale weather station network) has served a community of more than 1,400 public safety officials (emergency managers, fire officials, law enforcement, etc.) across Oklahoma through a weather data and training program called Oklahoma’s First-Response Information Resource System using Telecommunications (OK-First). OK-First provides free weather and radar data interpretation classes to eligible public safety officials and, following successful completion of training, password-protected access to weather data tools including a website and software. The objective of OK-First when it began was to fill significant gaps in weather product training and data access for Oklahoma’s public safety community. Though the core mission remains the same 20 years later, many aspects of OK-First have evolved over time, including participant membership, training curriculum, formats of training, training requirements, website and software technology, and program feedback. The purpose of this paper is to provide an update on the Mesonet’s OK-First program, with a particular focus on training, tools, and the impact it has had on the public safety community.
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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 | 0.013 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".