Bibliographic record
Abstract
The authors have no conflicts of interest. ACKNOWLEDGEMENTS The authors would like to acknowledge funding from the Canadian Institutes of Health Research Grant # 102627 for the project: “Does Minimum Pricing reduce the burden of disease and injury attributable to alcohol ” (Principal Investigator: Tim Stockwell). We would also like to acknowledge gratefully the assistance given by Drs Jürgen Rehm and Lana Popova at the Centre for Addiction and Mental Health, Ontario for permission to use material developed for their study on the economic costs of substance abuse in Canada (Rehm et al, 2006). Professor Petra Meier of Sheffield University is a co-investigator on the grant and while she was on maternity leave for the implementation of this study she played a leadership role in developing the initial version of the Sheffield Model which has now been applied to multiple jurisdictions. We gratefully acknowledge receipt of data from the Liquor Control Board of Ontario which were critical to some of the analyses employed. Data were also used for British Columbia that are reported as part of the BC Alcohol and Other Drug Monitoring Project (see: www.AODmonitoring.ca). Does minimum pricing reduce the burden of disease and injury attributable to alcohol? 05/12/12
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.165 | 0.035 |
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".