Frequencies of Morphological Characteristics in Two Contemporary Forensic Collections: Implications for Identification<sup>*</sup>
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Positive identification relies on comparison of antemortem and postmortem data. Some identifications are based on morphological features such as fracture, pathological condition, and surgical hardware, despite little literature indicating the frequencies of such traits. This study examines whether such features are sufficiently rare as to be deemed individualizing. Data were collected on two modern North American skeletal collections (N=482 individuals). Presence/absence of features was scored by skeletal element and side. Results indicate that frequencies vary by geographic region (higher frequency of fractures and pathological conditions in New Mexico while individuals in Tennessee were more likely to have surgical interventions), many features such as fractures are remarkably common and that even suites of traits may not be individualizing. Caution is warranted when using written data rather than radiographic comparisons as the primary source of identification. The implications of these findings to missing person databases are also discussed.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it