in Canada and Greenland Using Dental Pb Isotopes Derived from Local Geochemical Environments
Bibliographic record
Abstract
ABSTRACT. The chemical composition of animal tissues such as teeth appears to reflect an individual’s exposure to its geochemical environment. In this study, the lead (Pb) isotope composition of dental cementum was used to investigate the stock structure of Atlantic walrus (Odobenus rosmarus rosmarus) in the Canadian Arctic and Greenland. The 12 communities providing walrus samples for this study represent most of the Canadian and Greenlandic villages where walrus still form an important part of the traditional Inuit diet. Significant differences between locations in mean Pb isotope ratios and the limited overlap of the ranges of values indicate that each village harvested walrus herds that exploited substantially different geological/geographical habitats. This geographic segregation based on isotopic signatures suggests that most walrus stocks (i.e., the groups of walrus that interact with hunters at each community) are more localized in their range than previously thought. 208Pb/207Pb and 208Pb/204Pb were the most important stock discriminators, reflecting the influence of local geological Th/U composition (i.e., 208Pb) on Pb isotope composition in walrus teeth. 204Pb-based isotope ratios in walrus were consistently higher (more radiogenic) and more homogeneous than those in regional terrestrial bedrock, a difference probably due to selective leaching of radiogenic Pb from mineral phases into seawater and mixing during weathering and transport. Dental Pb isotope signatures may have widespread application to stock discrimination of other coastal marine mammal species. Key words: walrus, Odobenus rosmarus rosmarus, teeth, lead isotopes, stock discrimination RÉSUMÉ. La composition chimique de tissus animaux tels que les dents semble refléter l’exposition d’un individu à son milieu
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".