The rise of 3D-printed firearms in Quebec (Canada): casework overview
Bibliographic record
Abstract
The accessibility of 3D-printed firearms (3DPF) has increased in recent years, due in part to advancements in 3D printing technologies and a decrease in 3D-printer costs. The emergence of 3DPFs has raised security concerns, as their lack of serial numbers makes them difficult to trace. The province of Quebec is not spared by the rise of 3DPFs and has had to adapt to stay ahead. This paper presents data from all 3DPFs and 3D-printed related exhibits analyzed at the Laboratoire de sciences judiciaires et de médecine légale (LSJML) from the first case identified in 2016 to 2023. Recently, a diversification of 3D-printed related exhibits has been observed. The most frequent 3DPF model received is a Glock-type frame which, once assembled, can perform as well as a commercial firearm. However, assembling any 3DPF can be challenging due to the time and parts needed to render it functional. An interdisciplinary team specializing in 3D-printed firearms was created at LSJML to develop its expertise and support the judicial system in its need for improvement in the forensic examination of 3DPFs. Finally, 3DPFs present challenges in court due to their novelty, as no consensus has been established on the required examination for these firearms.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".