Le développement d’une approche systématique au profilage du tabac de contrebande
Bibliographic record
Abstract
Tobacco smuggling in Canada is a pervasive and growing criminal phenomenon, associated with significant social and economic issues.This research project aims to apply intelligence procedures to contraband tobacco through profiling.By leveraging concepts from forensic science and criminology, profiling the physical (production, packaging) and chemical characteristics of cigarette packs could enable a systematic comparison approach.It aims to support the analysis and understanding of crime related to the supply of raw materials, the production of contraband cigarette packs, and their distribution, particularly in the context of organized crime.This research project is conducted in collaboration with the Canadian Border Services Agency (CBSA), a key point of contact for all matters related to tobacco trafficking.The cigarette packs provided for this study comes from seizures made by various police services.Profiling consists of establishing a profile of a targeted subject based on characteristics observed on material traces, making it possible to compare similarities or differences with other profiles.To do this, the physical (dimensions, folding, striations, printing, ...) and chemical (chemical composition, UV-Vis of cardboard, plastic and aluminum foil) characteristics of cigarette packaging components were observed, measured and coded.Observations included examinations by the naked eye, stereomicroscope under various lighting conditions (ultraviolet, infrared, filtered light), and an infrared spectrometer.Multivariate statistical methods have then been used to assess the similarities between different profiles.The results of the analyses establish links between the cigarette packs and the seizures based on the groups obtained.In addition, these results are verified with circumstantial information provided by the CBSA regarding the seizures of contraband packs included in this project.The results allow us to formulate hypotheses on supply, production and distribution networks.
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".