Geedipally, Lord and Dhavala 1 A Caution about using Deviance Information Criterion While Modeling Traffic Crashes Technical Communication
Bibliographic record
Abstract
Geedipally, Lord and Dhavala 2 The Poisson-Gamma (PG) or negative binomial (NB) model still remains the most popular method used for analyzing count data. In the software WinBUGS (or any other software used for Bayesian analyses), there are different ways to parameterize the NB model. In general, either a PG (based on the Poisson-mixture) or a NB (based on the Pascal distribution) modeling framework can be used to relate traffic crashes to the explanatory variables. However, it is important to note that the way the model is parameterized will influence the output of the Deviance Information Criterion (DIC) values. The objective of this short study is to document the difference between the PG and NB models in the estimation of the DIC. This is especially important given that the NB/PG model is still the most frequently used model in highway safety research and applications. To accomplish the study objective, PG and NB models were developed using the crash data collected at 4-legged signalized intersections in Toronto, Ont. The study results showed that there is a considerable difference in the estimation of the DIC
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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 | 0.041 | 0.191 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.004 | 0.015 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".