The validation of 3D virtual comparison microscopy (VCM) in the comparison of expended cartridge cases
Bibliographic record
Abstract
In this study, the Cadre TopMatch-3D scanner and associated virtual comparison software was evaluated to determine whether this technique is a valid and reliable method of conducting cartridge component comparisons. That is, if virtual comparison microscopy (VCM) produces results at least equivalent to those generated through traditional light comparison microscopy (LCM), the method would be deemed valid for use in an operational setting. Particular emphasis was placed on the capability to render same source conclusions. Of the 40 true identifications available to each examiner, corresponding to a total of 520 comparisons, positive identifications were made more frequently using VCM as compared to traditional LCM where inconclusive conclusions were provided at a higher rate. VCM produced a higher sensitivity (88.41%) and specificity (13.64%) rate than LCM, 80.08% and 12.50%, respectively. Based on the findings of the study, considered together with the benefits that VCM offers in the name of efficiency, it became apparent that Cadre's 3D scanning microscope and its associated virtual comparison software tested in this study is an appropriate and valid technique for conducting comparisons of expended cartridge cases and can be implemented into routine casework for that purpose.
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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 | 0.022 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".