Experimental probabilistic hazard information in practice: Results from the 2008 EWP Spring Program
Bibliographic record
Abstract
The National Oceanic and Atmospheric Administra-tion’s (NOAA) National Weather Service (NWS) has recently transitioned to ”storm-based ” warnings from county-based warnings. These warnings are increasingly used by graphical applications for television, the Inter-net, and cell phones to better communicate specific in-formation about hazardous weather. With the rapid up-dates in technology and communication, the NWS can continue to build upon the storm-based warnings to bet-ter communicate specifics in uncertainty, space, and time to advanced and special-need users. During the 6 week period of 27 April-7 June 2008, the NOAA Hazardous Weather Testbed in Norman, OK hosted multiple visiting NWS and Environment Canada forecasters for the Experimental Warning Program. The forecasters had the opportunity to issue probabilistic guidance on several real-time severe weather events across the continental United States and an archive event from 13 August 2007 in northeast North Dakota. Each forecaster was asked to identify areas of a storm where a threat was possible, either at the current time or near future (less than 60 min) and determine a probability as-sociated with that threat (current and at a chosen future time). The project focused on three different threats: Tor-nado, Hail (greater than.75 in), and Wind (greater than 50 kts). Probabilistic hazard forecasts made throughout the six week period and from the archive event will be compared to storm data as well as the high resolution data from the Severe Hazards Analysis and Verification Experiment (SHAVE) to determine skill and reliability of the forecasts and how this guidance should be updated for future use. In addition, feedback from visiting fore-casters concerning product use and workload as well as societal impacts of such products are discussed.
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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 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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; 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".