Canine Surrogacy Approach: Applications for Studying North American Pre-Colonial Diets
Bibliographic record
Abstract
Studying human diets through isotopic signatures can come with multiple challenges, primarily the availability and feasibility of utilizing human tissues, such as bone, teeth, hair, or coprolites. This may be due to preservation issues, the destructive nature of the analysis, ethics, or legislature. To mitigate these issues, it has become increasingly popular for researchers to utilize tissues from animals. In order for this method to be used successfully, an animal that would have consumed a diet similar to that of its human companions must be utilized. Due to the close relationship between humans and dogs in many past cultures worldwide, a method called the Canine Surrogacy Approach (CSA) has proved successful in many regions where dogs were known to have subsisted on human foods. This method is useful for colonized regions, as the archaeological remains of Indigenous people are often only subjected to bioarchaeological analysis if descendant groups grant explicit permission. This paper highlights and discusses the usefulness of this method in North America with reference to ethnographic and ethnohistoric accounts of dog provisioning. Three case studies are presented, which exhibit the applications of the CSA in different regions of the United States and Canada. Each case study approaches the CSA in different ways, showcasing the various applications of this method utilizing a variety of bioarchaeological remains. In closing, the usefulness of applying the CSA method in future studies of the dispersal of maize in the period before colonization in Canada is emphasized.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".