DURING ALCOHOL INTERLOCK USE, ELEVATED BACS IN MORNING OR DECLINING VEHICLE USE OVER TIME PREDICTS POST-INTERLOCK RECIDIVISM
Bibliographic record
Abstract
The alcohol interlock record is the first extensive behavioral record that has ever been available to help researchers understand DWI offenders. The average interlock device in Alberta Canada logs over 2300 breath tests during an average 9 months of interlock use. Analysis of the patterns of violations makes available new predictive information for scaling driver risk. Recent work (Marques et al., 2001, Marques et al., 2002) has shown the overall rate of elevated BAC tests during the first several months of use strongly predicts repeat DWI after the interlock has been removed. This report further analyzes the record of 5.5 million BAC tests in Alberta and finds that even after accounting for strong predictors of post-interlock recidivism such as prior repeat DWI and the overall prediction based on higher rates of failed interlock BAC tests, if the BAC tests failures occur in the morning (e.g., BAC still >=.04 gm/dl presumably from previous night drinking) there is an additional 45% higher likelihood of later having a new repeat DWI. Also, evidence is presented that offenders who decrease their overall interlock vehicle use (fewer tests taken over interlock use time) have a higher likelihood of recidivism post-interlock. (A) For the covering abstract of the conference, see ITRD Abstract No. E201067.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".