A review of the literature on the applications of machine learning in forensic anthropology
Bibliographic record
Abstract
Applications of machine learning (ML) models in forensic anthropology have increased in the last half decade. It is, therefore, important to understand the context in which machine learning models are being used in this discipline. The aim of this paper is to provide the current state of machine learning applications in forensic anthropology through a systematic review process of the literature. This paper provides a descriptive summary of existing literature, rather than a deep critical analysis of the methodological robustness. The literature search was performed using Scopus and Web of Science from 1987 to 2024. Eligible studies were investigated if they had a forensic anthropological focus with an application of machine learning. A total of 167 papers were analyzed after the exclusion criteria were applied. The results of this paper demonstrate that there is a wide range of machine learning model applications in forensic anthropology, utilizing diverse bones, applied to all aspects of the biological profile and some aspects of trauma analysis. Through this review, it is also encouraged to use ML models on underutilized skeletal elements to optimize the pattern recognition capabilities of machine learning for validation of forensic anthropological assessments. Validating ML models on less commonly analyzed skeletal elements will increase the skeletal elements that can be utilized to assist in identifying and repatriating individuals. The review also demonstrates that there is an increased need to provide a comprehensive description of the machine learning applications to increase transparency, interpretability, and further validation in forensic anthropological assessments.
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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 | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".