Gaps in toxic industrial chemical model systems: Improvements and changes over past 10 years
Bibliographic record
Abstract
Abstract To assess the hazards of the releases of toxic industrial chemicals (TICs) to the atmosphere, comprehensive model systems are often used, which begin with the scenario definition and end with an estimate of health risk. In 2008 and 2010, the US Department of Homeland Security and Defense Threat Reduction Agency sponsored reports that identified knowledge gaps in TIC modeling. The current paper discusses which of the knowledge gaps were satisfactorily resolved in the past 10 years by new theoretical and experimental research, such as the 2010 and 2015–2016 Jack Rabbit field experiments. For example, the linked source emissions and transport and dispersion (T&D) models have been shown, in comparisons with Jack Rabbit II observations, to not have large mean biases. Consequently, the T&D models are less likely to be the cause of model system overpredictions of casualties observed after large TIC accidental releases, such as the Festus, Macdona, and Graniteville chlorine railcar incidents. It may be that the deposition models and/or the health effects models still need improvement. In addition to comments on the knowledge gaps identified 10 years ago, a few new knowledge gaps are addressed, such as indoor T&D and deposition, and estimating the magnitude of the saturation deposition value for various substrates and chemicals.
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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 | 0.045 | 0.087 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".