One Drink to a Lifetime of Drinking: Temporal Structures of Drinking Patterns
Bibliographic record
Abstract
This article presents the proceedings of a symposium at the 2001 Research Society on Alcoholism Meeting in Montreal, Canada. The cochairs were Paul J. Gruenewald and Marcia Russell. The focus of the symposium was on mathematical, methodological, and statistical approaches to the assessment of drinking patterns from short (daily and monthly) to very long periods (the life course) of time. The research presented in the symposium argues that (1) model-based approaches to analyzing drinking patterns can provide comprehensive bases for assessing drinking risks, (2) data acquisition technologies that track daily drinking over long periods of time can illuminate unique features of drinking associated with abuse and dependence, and (3) retrospective data can be used to assess life-course trajectories of drinking associated with chronic problem outcomes. Each of the presentations points toward an integrated approach to understanding acute and chronic risks related to alcohol use. The presentations were (1) Mathematical models of current drinking, by Paul J. Gruenewald and Fred Johnson; (2) Mathematical models of drinking problems, by John Light and Rob Lipton; (3) Patterns of drinking ascertained from daily data aggregated across 24 months, by John Searles; and (4) Cognitive lifetime drinking histories and natural histories of drinking, by Marcia Russell, Paul J. Gruenewald, Fred Johnson, Maurizio Trevisan, Jo Freudenheim, Paola Muti, Ann Marie Carosella, and Thomas H. Nochajski.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".