Variation within global cigarette brands in tar, nicotine, and certain nitrosamines: analytic study
Bibliographic record
Abstract
Editor,—While the content of food, pharmaceutical products, drugs, and many other consumer goods are tightly regulated by governments, tobacco products, surprisingly, are not. Tar and nicotine yields of cigarettes have progressively, but not universally, appeared on cigarette packets and advertising since 1967. These figures have been used to justify terms such as “light” and “mild” in descriptive advertising. In 1981 a US public health report concluded: “the preponderance of scientific evidence strongly suggests that the lower the “tar” and nicotine content of the cigarette, the less harmful would be the effect.”1 Some early reports concluded, plausibly, that a decrease in lung cancer mortality could be ascribed to smoking reduced tar cigarettes, although more recent data2 suggest that there is little if any difference in the long term outcome of smoking “low tar” as against “regular” cigarettes. Further there has been an increase in adenocarcinoma relative to squamous carcinoma, …
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".