Adherence of Forensic Odontologists to the ABFO Bite Mark Guidelines for Suspect Evidence Collection
Bibliographic record
Abstract
Boards and associations within forensic science have long been accepted as vehicles for the development and dissemination of protocols and recommendations for practice. Recent controversies surrounding bite mark analyses have brought the methods and practices of forensic dentists to the attention of both the courts and the media. In the mid-eighties the American Board of Forensic Odontology developed guidelines for bite mark analysis in response to unfavorable commentaries on the discipline by legal observers. The purpose of this study is to examine the adherence of board certified and noncertified forensic dentists to the guidelines for collection of evidence from bite mark suspects. A questionnaire was employed during an American Academy of Forensic Sciences meeting. Results showed that, in general, when the odontologists collected evidence they did adhere to the guidelines, although collection of salivary samples was not common. Of concern is the large number of odontologists who do not collect their own evidence from suspects. Police officers or other individuals often perform this task and therefore the guidelines must be disseminated to these groups to ensure that the maximum yield is obtained from bite mark evidence. A review of the materials used to collect evidence is also included with details of applications in forensic science.
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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 | 0.086 | 0.234 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".