Military Forensic Archaeology: The Process and Recovery of U.S. Missing‐in‐Action (MIA) Service Personnel
Bibliographic record
Abstract
ABSTRACT The United States (U.S.) Department of Defense's (DoD) Defense POW/MIA Accounting Agency (DPAA) is the largest skeletal identification laboratory in the world and receives several hundred cases per year for identification related to Missing‐in‐Action (MIA) U.S. service members. While there are various sources for these accessions, by far the largest source is through unilateral or bilateral (done in cooperation with another governmental entity or nongovernmental organization) as well as public‐private partnership excavations. While the DPAA‐style military forensic archaeology follows archaeological principles and recording standards, the work is goal‐oriented, evidence/trace‐driven, and time‐constrained—a common occurrence with cultural resource management sites or salvage/heritage archaeology throughout the world. The focus herein is to present the basic tenets and principles that guide the DPAA military forensic archaeologist in decision‐making regarding items of relevance that assist the excavation process as well as guide the individual identification process once the field operations cease. DPAA forensic archaeologists use a mindset of flexibility of standard archaeological methods and principles. This flexibility is focused on these ideals, such that a large amount of surface area must be excavated in an extremely limited time. The forensic concepts of trace relevance and trace value at various levels (a “nested” approach) of site formation, time asymmetry, and biological identification are tied to these archaeological principles and context to support the goal of forensic identification of individual missing U.S. service personnel. This article is categorized under: Crime Scene Investigation > Crime Scene Documentation and Visualization Forensic Anthropology > Anthropology in Mass Disaster & War Crime Contexts Forensic Anthropology > Forensic Archaeology
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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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".