Paired stable carbon and oxygen isotope analyses of human enamel for forensic human geolocation: An exploratory study
Bibliographic record
Abstract
Stable carbon isotope analysis has proven utility for reconstructing dietary information in humans in past populations. The usefulness of stable carbon for forensic geolocation has been little investigated, largely because of the globalization of the human diet seemingly rendering it inconsequential. This study queried this assumption at a country-wide level on a known sample group. Stable carbon isotope values were obtained from human enamel with known biographical and geographical information to determine whether stable carbon, when paired with stable oxygen isotope values, could differentiate Canadians from non-Canadians. Samples originating outside of Canada were separated into three regions, and a linear discriminant analysis was used to generate discriminant functions that best separate the regions according to the stable carbon and oxygen isotope values. The results revealed two functions, where the first function explained 92.1% and the second 7.9% of the variance. Although some overlap in stable carbon and oxygen values was observed for individuals from both the United States and Canada, differences were observed between those from Canada and other geographical regions. This study demonstrated that pairing the dietary isotope carbon with the geolocator stable oxygen isotope, produced an interesting separation geographically, one that might well be helpful when attempting a geolocation query on unknown human remains.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".