Human and dog Bayesian dietary mixing models using bone collagen stable isotope ratios from ancestral Iroquoian sites in southern Ontario
Bibliographic record
Abstract
Abstract Under the archaeological canine surrogacy approach (CSA) it is assumed that because dogs were reliant on humans for food, they had similar diets to the people with whom they lived. As a result, the stable isotope ratios of their tissues (bone collagen and apatite, tooth enamel and dentine collagen) will be close to those of the humans with whom they cohabited. Therefore, in the absence of human tissue, dog tissue isotopes can be used to help reconstruct past human diets. Here δ 13 C and δ 15 N ratios on previously published dog and human bone collagen from fourteenth-seventeenth century AD ancestral Iroquoian village archaeological sites and ossuaries in southern Ontario are used with MixSIAR, a Bayesian dietary mixing model, to determine if the dog stable isotope ratios are good proxies for human isotope ratios in dietary modeling for this context. The modeling results indicate that human dietary protein came primarily from maize and high trophic level fish and dogs from maize, terrestrial animals, low trophic level fish, and human feces. While isotopes from dog tissues can be used as general analogs for human tissue isotopes under CSA, greater insights into dog diets can be achieved with Bayesian dietary mixing models.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".