The application of a CART model for forensic human geolocation using stable hydrogen and oxygen isotopes
Bibliographic record
Abstract
Abstract The utility of stable hydrogen and oxygen isotope analysis of human tissues for geolocation is an important area of study within forensic science. This study aimed to first validate the latitudinal relationship between stable hydrogen and oxygen isotopes in drinking water and human keratinous tissues through the analysis of human samples with known geographical origin. And secondly, to explore the use of classification and regression tree (CART) models to geographically classify individuals based on the stable isotope values of tissues themselves. Human hair and toenails were collected from four distinct study sites across Canada. The comparison of stable isotope values in drinking water and human tissues produced low R 2 values indicating that linear models may not fully explain the variation observed for both hydrogen and oxygen values. Additionally, large intrapopulation variations were observed for Canadian cities and highlights the importance of understanding the regional isotopic spread of human values. Further, this study demonstrated that a closed group of unknown individuals known to have originated from a limited number of geographically distinct regions may be classified into their respective groups through the use of CART models. The potential for the CART model approach for human geolocation presents a promising new tool.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".