Additional file 1 of The smoking and vaping model, A user-friendly model for examining the country-specific impact of nicotine VAPING product use: application to Germany
Bibliographic record
Abstract
Additional file 1: Supplementary Table 1. Smoking and Vaping Model, recommended definitions for cigarette use and NVPs use status. Supplementary Table 2. SAVM cigarette to nicotine and vaping product use switching rates from the US Population Assessment of Tobacco and Health (PATH) survey 2013-2017. Supplementary Table 3. Smoking prevalence (%), validation of Germany-SAVM against the Eurobarometer-Germany, by age and gender, 2014–2020. Supplementary Figure 1a. Validation of Germany-SAVM male smoking prevalence vs. German Microcensus 2017. Supplementary Figure 1b. Validation of Germany-SAVM female smoking prevalence vs. German Microcensus 2017. Supplementary Figure 2a. Validation of Germany-SAVM NVP use prevalence vs. the German Study on Tobacco Use (DEBRA). Results for adults ages 18 to 24 years. Supplementary Figure 2b. Validation of Germany-SAVM NVP use prevalence vs. the German Study on Tobacco Use (DEBRA). Results for adults ages 25 and above. Supplementary Table 4. The Germany SAVM model estimates for all cohorts (ages 18-99) with new births for 2012-2060. NVP risks at 15% those of excess smoking risks. Supplementary Table 5. The Smoking and Vaping Model modeling assumption and its implications.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.584 | 0.086 |
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".