A simple carbon isotope model for reconstructing prehistoric human diet
Bibliographic record
Abstract
A compilation of experimental animal data shows that neither delta13C(collagen) nor delta13C(apatite) nor Delta13C(CO-AP) indicate diagnostic reconstructions of diet, diet energy and diet protein. In contrast, plots of delta13C(collagen) against delta13C(apatite) provide a model of three regression lines (C3, C4, and marine diet protein) where position on each line indicates the energy source (C3, C4, or mixed). Neither body size nor trophic position appears to affect these relationships. Modern free-ranging, terrestrial fauna do not fit the model perhaps because they, unlike the experimental fauna, mainly use fermentation rather than digestion during energy metabolism. Archaeological humans fall as expected based on associated floral and faunal evidence. Foraging people plot at positions expected from associated C3 fauna and plants. Those from Cahokia plot, as expected, from associated deer, nuts, and maize whereas people from nearby smaller sites plot in positions consistent with eating more fish. Agriculturists from Ontario and Grasshopper Pueblo plot consistent with dependence on fish by the former and on turkeys by the latter. In Tierra del Fuego, people from interior regions ate more terrestrial fauna, as suggested by ethnohistoric reports, than did people from the coast. In the Southwestern Cape in South Africa individuals late in the sequence have pure C3 diets whereas ones early in the sequence ate marine protein as suggested by independent archaeological evidence. People on San Nicolas Island depended on C4 plants in contrast to other islands off California's coast. This simple model provides more detailed and precise dietary information than do individual isotopic measures.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".