The use of a novel tobacco treatment process to reduce toxicant yields in cigarette smoke
Bibliographic record
Abstract
The US Institute of Medicine has encouraged the pursuit and development of potential reduced-exposure products (PREPs) - tobacco products that substantially reduce exposure to one or more tobacco toxicants and can reasonably be expected to reduce the risk of one or more specific diseases or other adverse health effects. One potential approach is to reduce levels of some smoke toxicant precursors, such as proteins and polyphenols, in tobacco. We describe a treatment process involving aqueous tobacco extraction and treatment with protease; filtration of the extract to remove peptides, amino acids and polyphenols, and recombination of extract and treated tobacco. The process reduced levels of protein nitrogen (59%), polyphenols (33-78%) and nicotine (12%) while sugars increased 16%. ISO mainstream smoke yields of 43 toxicants were measured from cigarettes containing treated tobaccos; lower yields of tar, nicotine, carbon monoxide (16-20%), acrylonitrile, ammonia, aromatic amines, pyridine, quinolene and hydrogen cyanide (33-51%), tobacco specific nitrosamines (25-32%); phenolics (24-56%), benzene (16%), toluene (25%) and cadmium (34%) were obtained. There were significantly increased yields of formaldehyde (49%) and isoprene (17%). Reductions in sidestream yields of nitrogenous smoke toxicants and increases in sidestream yields of several carbonyls, benzo(a)pyrene and isoprene were also observed.
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 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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".