Digital image analysis of cigarette filter staining to estimate smoke exposure
Bibliographic record
Abstract
Sufficient variation exists in how people smoke each cigarette that the number of cigarettes smoked daily and the years of smoking represent only crude measures of exposure to the toxins in tobacco smoke. Previous research has shown that spent cigarette filters can provide information about how individuals smoke cigarettes. Digital image analysis has been used to identify filter vent blocking and may also provide an inexpensive, unobtrusive index of overall smoke exposure. A total of 1,124 cigarette butts smoked by 53 participants in a smoking topography study were imaged and analyzed. Imaging showed test-retest reliability of more than 95% among those smoking their own brand. Mean color scores (CIELAB system) showed acceptable stability (>.60) across days, paralleling the basic stability of smoking topography measures across waves. A principal components scoring showed that center tar staining, edge tar staining, and their interaction were significantly related to total smoke volume, accounting for 73% of the variation. Estimated smoke volume was a significant predictor of salivary cotinine when accounting for cigarettes smoked per day. These data suggest that digital image analysis of spent cigarette butts can serve as a reliable proxy measure of total smoke volume.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.006 | 0.001 |
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".