Design and chemical evaluation of reduced machine-yield cigarettes
Bibliographic record
Abstract
Experimental cigarettes (ECs) were made by combining technological applications that individually reduce the machine measured yields of specific toxicants or groups of toxicants in mainstream smoke (MS). Two tobacco blends, featuring a tobacco substitute sheet or a tobacco blend treatment, were combined with filters containing an amine functionalised resin (CR20L) and/or a polymer-derived, high activity carbon adsorbent to generate three ECs with the potential for generating lower smoke toxicant yields than conventional cigarettes. MS yields of smoke constituents were determined under 4 different smoking machine conditions. Health Canada Intense (HCI) machine smoking conditions gave the highest MS yields for nicotine-free dry particulate matter and for most smoke constituents measured. Toxicant yields from the ECs were compared with those from two commercial comparator cigarettes, three scientific control cigarettes measured contemporaneously and with published data on 120 commercial cigarettes. The ECs were found to generate some of the lowest machine yields of toxicants from cigarettes for which published HCI smoke chemistry data are available; these comparisons therefore confirm that ECs with reduced MS machine toxicant yields compared to commercial cigarettes can be produced. The results encourage further work examining human exposure to toxicants from these cigarettes, including human biomarker studies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".