A statistical method for the evaluation of projectile dispersion
Bibliographic record
Abstract
As part of a research program, an extensive study on the dispersion characteristics of eight different 7.62 × 51 mm ammunition types was conducted. The paper presents the main steps in the experimental and analytical process carried out to evaluate, namely to measure and compare, the dispersion characteristics of the ammunitions; namely, (1) identify the number of rounds to fire in the trials, (2) establish a test plan and the setup for the precision trials, (3) fire the rounds, following an established protocol for the experiments, (4) collect the impact points, and measure the performance through statistical measures, (5) perform a statistical analysis of dispersion applied to the results obtained in the trials, and (6) conclude on the ammunition characteristics. In particular, the paper proposes a statistical method to evaluate the precision of ammunitions fired with precision (Mann) barrels. The practical method relies on comparison of confidence intervals and hypothesis testing on the standard deviation of samples, namely the impact points. An algorithm is proposed to compare the variances of two or more populations of ammunitions.
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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 | 0.030 | 0.087 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".