TOWARDS A DROP-IN REPLACEMENT SUBSONIC CAPABILITY FOR NATO SMALL ARMS
Bibliographic record
Abstract
While subsonic ammunition has been of military interest for years, particularly for covert operations, significant limitations of current offerings have prevented its adoption including the inability to cycle an automatic action. This and other limitations were investigated through a design exercise to establish the feasibility of a drop-in replacement relevant to the Canadian Armed Forces using commercial off the shelf components. A survey and stability analysis of commercially available 5.56 mm projectiles led to the selection of the 90 grain Sierra Matchking for this design. Three different cartridge configurations were investigated in order to evaluate the effect of reducing cartridge volume on key internal ballistic variables. A cycling impulse model was developed in order to rank candidate designs on their ability to cycle a prescribed automatic weapon. Cartridge volume, in terms of standard cartridge, straight internal bore, and custom internal diameter to realize 95% load density, was explored as a means of increasing the load density of subsonic ammunition in order to reduce muzzle velocity variance and thus increase accuracy. However, the accuracy gained by reducing cartridge volume is shown to cause significant tradeoffs in cycling impulse. Live fire testing was performed in order to determine the minimum cycling impulse required to cycle the test weapon. The results also indicate that there may exist a threshold load density above which load density does not play a significant role in muzzle velocity variance. Thus, an objective of maximizing load density may overconstrain the design for minimal benefit.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".