UDINEE: Evaluation of Multiple Models with Data from the JU2003 Puff Releases in Oklahoma City. Part II: Simulation of Puff Parameters
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Bibliographic record
Abstract
The capabilities of nine atmospheric dispersion models in predicting near-field dispersion from puff releases in an urban environment are addressed under the Urban Dispersion INternational Evaluation Exercise (UDINEE) project. The model results are evaluated using tracer observations from the Joint Urban 2003 (JU2003) experiment where neutrally-buoyant puffs were released in the downtown area of Oklahoma City, USA. Sulphur hexafluoride concentration time series measured at ten sampling locations during four daytime and four night-time puff releases are used to evaluate how the models simulate the puff passage at the measurement locations. The neutrally-buoyant puff releases in the JU2003 experiment are the closest scenario to radiological dispersal device (RDD) releases in urban areas, and therefore, UDINEE is a first step towards improving the emergency response to an RDD explosion in the urban environment. We investigate for each puff and sampler the model capability of simulating the peak concentration; the peak and puff arrival times; and time duration, defined as the period over which concentrations exceed 10% of the peak concentration. This analysis points out differences on the performance of models: the fraction within a factor-of-two values ranges from 0.10 to 0.6 for peak concentration, from 0 to 1 for the peak and arrival times, and from 0 to 0.8 for the time duration. The results reveal that the characteristics of the release site largely influence the model simulation as it affects initial puff size and the initial downwind spread of the puff.
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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 | 0.001 | 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) | 0.001 | 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 it