Mainstream smoke emissions of Australian and Canadian cigarettes. Nicotine Tob Res 2007;9:835–44
Bibliographic record
Abstract
We investigated how mainstream smoke emissions vary and interrelate in 15 Australian and 21 Canadian brands, using public emissions disclosures from 2001. These disclosures provided emission data for 40 hazardous agents under both standard and intensive ISO testing conditions. Our analyses focused on ‘‘adjusted emissions’ ’ (i.e., emissions per milligram of nicotine yield) for 13 selected agents. Adjusted emissions differed significantly by ISO testing condition for 9 of the 13 selected agents. Intensive condition adjusted emissions were strongly negatively correlated for several agent pairs. Country and manufacturer variables were the strongest predictors of intensive condition adjusted emissions for 8 of the 13 selected agents and significant predictors for all of them. Taken together, these results suggest potential for the intent of emission limits to be undermined by risk swapping (in which one specific exposure is reduced within a group at the cost of another’s exposure increasing) and risk shifting (in which a specific exposure is reduced within a group at the cost of that exposure’s increasing within another group).
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".