Urban Dispersion Virtual Workshop: Designing the Next Generation Urban Dispersion Field Programs
Bibliographic record
Abstract
The overarching objective of this workshop is to start the process of planning and implementing the “Next Generation of Urban Dispersion Studies”. We have organized this program to bring people interested in this complex problem together. We have representation from different disciplines, different agencies, and different academic institutions. The workshop is organized around several questions: • What are the needs of the emergency response community and how do we design models and experiments to address them? • What model improvements do the user communities need? • How do we design tracer release and sampling to address questions temporal (e.g., diurnal and seasonal variation) and spatial (e.g., land/urban/water gradients) for the broadest applicability? • What are the next-generation measurements needed to improve the data sets? • How do we implement the next generation urban dispersion experiments? The Virtual workshop format is exciting. We view this as an experiment which, if successful, will bring a large group of people together from the community to design the next generation urban experiments. We have many agencies participating: DOE-BER, DHS, FEMA, NNSA, NARAC, DTRA, NYPD, and NIST. The registration list includes over 40 people including participants from well beyond the US including Spain and Canada.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.024 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.025 | 0.005 |
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".