Analyses of various clay bite-mark impressions that correlate back to their respective skeletal dentition
Bibliographic record
Abstract
Demonstrating the importance of bite-mark evidence within the forensic science community is extremely valuable in order to retain its legitimacy. This was illustrated by using techniques and analyses to match a bite-mark impression back to the specimen in which it originated. Bite-marks are known as a number of bruised markings on the skins surface, often in a semi-lunar shape produced by a human with a particular set of teeth. When referring to a particular set of teeth, class and individual characteristics need to be examined within that dentition. 20 bite-mark impressions were created within clay using 10 skeletal mandibles and 10 skeletal maxillas. 60 trials were performed in order to examine whether or not the given cast and impression are a match or no match to one another. 57 out of 60 trials were successful, giving a 95% success rate in determining the correct outcome. However, 3 of out 60 trials were unsuccessful. The success rate of the performed trials provides evidence that bite-marks are fundamentally important in forensic investigations. The purpose of this research was to accurately match various bite-mark impressions back to the specimen that produced it in order to prove the worth of bite-mark evidence in a court of law.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".