Contraband tobacco: Systematic profiling of cigarette packs for forensic intelligence
Bibliographic record
Abstract
Tobacco smuggling remains a widespread illegal activity in Canada, associated with important social and economic impacts, and often linked to organized crime. This study explores the application of forensic profiling as an intelligence tool to support the analysis of contraband cigarette production and distribution. Physical and chemical manufacturing characteristics of seized contraband cigarette packs, provided by the Canada Border Services Agency (CBSA), were observed and coded using macroscopic, microscopic, and spectroscopic techniques. Multivariate statistical analyses were then conducted to compare manufacturing characteristics between packs and identify potential links. The analyses highlighted links between cigarette packs and seizures based on shared manufacturing characteristics. The results and the identified groups were also compared with seizure data provided by the CBSA. The results demonstrate the relevance of forensic profiling to formulate hypotheses regarding shared production processes or supply networks. These hypotheses provide information that contributes to understanding tobacco smuggling and aim to examine how forensic intelligence can support law enforcement and measures to prevent and disrupt this criminal activity. A preliminary optimal procedure for applying forensic profiling in operational contexts targeting contraband tobacco was finally proposed. Despite limitations in the dataset creation that were beyond our control, this study represents a starting point for applying this scientific approach to tobacco smuggling. • Forensic profiling developed for contraband cigarette pack analysis • Physical and chemical traces analysed to establish production links • Multivariate statistics (FAMD) reveals links between seized cigarette packs • Forensic profiling enables hypotheses at printing, packaging, and supply levels • Optimized procedure proposed for forensic intelligence in contraband tobacco cases
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".