An Approach for Predicting Mainstream Cigarette Smoke Harmful and Potentially Harmful Constituent (HPHC) Yields
Bibliographic record
Abstract
To ensure quality, consistency, and supply of cigarette products, a manufacturer may change materials, which can affect its product portfolio. Rather than testing each product individually to determine the effect of a change, designed experiments can be conducted using a subset of products, and statistical modeling can be performed to determine the harmful and potentially harmful constituent (HPHC) yields for the remaining products. To demonstrate this, we selected 30 representative cigarette products covering a wide range of tobacco blends, ingredients, and design parameters from a manufacturer's portfolio. Sets of cigarette products used papers produced with one type of manufacturing technology (control products) and two additional cigarette papers (changed products). The physical characteristics of the changed products' papers were similar to the control products but were manufactured using alternative methods, which could lead to differences in their chemical composition. The experiment was controlled to minimize variations among products, manufacturing, and testing. Linear regression was used to model the relationship between HPHC yields of the tested products. Twelve randomly selected products were used for validation by comparing predicted to measured yields. Model predictions were robust; differences between measured and predicted values were within standard repeatability limits, demonstrating the feasibility of this approach.https://doi.org/10.21423/jrs-v07hannel
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".