Using momentum to determine serious bodily injury: an experimental study using pig eyes
Bibliographic record
Abstract
According to section 2 of the Criminal Code, a firearm is a barrelled weapon that can discharge a projectile capable of causing serious bodily injury or death to a person. Although not defined in the Criminal Code, serious bodily injury has been accepted in Canadian courts as the “penetration or rupture of an eye”. Classifying air guns may be difficult as they are usually not categorized as a firearm, but some are capable of meeting the criteria of a firearm. In previous studies, velocity and/or energy density were used to define a parameter V50, and a minimum energy density range where penetration may occur. This research project sought to evaluate the momentum of the projectile as a new parameter to determine if an air gun is capable of causing serious bodily injury. Three air guns, with five projectile types, were fired 10 times each into pig eyes. Results indicated that a minimum momentum value may be applied to each projectile of a different shape, regardless of the projectile’s mass. Minimum momentum values ranging from 0.026 kg*m/s (pointed) to 0.039 kg m/s (flat nose) were observed. It is hypothesised that impulse, which considers the cross-sectional area of projectiles, would be a universal parameter and more research should be done to test this.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".