DRAG, model of the Demand for Road use, Accidents and their Severity, applied in Quebec from 1956 to 1982.
Bibliographic record
Abstract
We develop a five-layer procedure to explain the impact of various factors on the monthly demand for road use and the number of road accident victims in Quebec as a whole from December 1956 until December 1982. The first layer consists of a model of total motor vehicle fuel sales (gasoline and diesel) from which we extract fuel sales for road use. This measure of transportation demand is the object of further analysis in the second layer and appears among the explanatory variables of both the number of accidents by type (material damages, non-mortal and mortal) and their severity (morbidity and mortality rates) which are explained in the third and the fourth layers, respectively. Results from the demand, accident and severity equations are combined to yield, in the fifth layer, evaluations of the effect of the factors considered on the number of persons hurt, killed, or on the total number of these road victims. The nine equations estimated define a REFERENCE MODEL which involves 60 distinct factors, approximately 40 of which are used in any given equation. In addition, a number of MODEL VARIANTS are studied: they require an additional 20 variables. Explanatory factors considered belong to seven major categories: demand, prices, motor vehicles (quantities and characteristics), network (legal regimes and police, service levels of modes, infrastructure quality), consumer characteristics (general, age, sex, vigilance), economic activities and trip purposes, and other (administrative, aggregation and seasonal/constant). The statistical model makes extensive use of flexible (Box-Cox) functional forms and simultaneously estimates the error distribution parameters (multiple autocorrelation and heteroscedasticity of a general nature) in order to obtain white noise constant variance equation residuals.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".