Radiocarbon Analysis of Human Remains: A Review of Forensic Applications
Bibliographic record
Abstract
Radiocarbon analysis of organic materials, with the comparison of values with those of the post-1950 modern bomb curve, has proven useful in forensic science to help evaluate the antiquity of evidence. Applications are particularly helpful in the study of human remains, especially with those displaying advanced decomposition of soft tissues. Radiocarbon analysis can reveal if the remains relate to the modern, post-1950 era and if so, also provide information needed to evaluate the death and birth date. Sample selection and interpretation of results must be guided by knowledge of the formation and remodeling of different human tissues, as well as contextual information and the approximate age at death of the individual represented. Dental enamel does not remodel and thus captures dietary radiocarbon values at the time of juvenile formation. Most other human tissues do remodel but at differing rates and therefore collectively offer key information relative to the estimation of the death date.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".