A Multi-Method Approach to Re-Associating Fragmented and Commingled Human Remains
Bibliographic record
Abstract
This article is a plain language summary of a Master’s thesis, completed in the Department of Archaeology and Anthropology at the University of Saskatchewan in 2020, that aimed to re-associate fragmented and commingled human remains from an Early Neolithic, about 7560–6660 years before present (HPD cal. BP; Weber et al., 2021; Bronk Ramsey et al. 2021), cemetery in Siberia, Russia. This thesis addressed the inability of existing osteological sorting methods to identify the remains of individuals from a collection that was largely broken and completely mixed. By developing a new multi-method approach, this project was able to identify the remains of seven people through the re-association process, and re-associate five of them. This was fairly close to the minimum number of nine individuals that were confirmed in this collection by counting non-repeating bones. This research has implications for the understanding of the culture-history in this area, the applicability of re-association methods to fragmented and commingled human remains, and efforts of reconciliation and repatriation.
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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 | 0.055 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| 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".