Quantitative diet reconstruction of a Neolithic population using a Bayesian mixing model (FRUITS): The case study of Ostorf (Germany)
Bibliographic record
Abstract
ABSTRACT Objectives The island cemetery site of Ostorf (Germany) consists of individual human graves containing Funnel Beaker ceramics dating to the Early or Middle Neolithic. However, previous isotope and radiocarbon analysis demonstrated that the Ostorf individuals had a diet rich in freshwater fish. The present study was undertaken to quantitatively reconstruct the diet of the Ostorf population and establish if dietary habits are consistent with the traditional characterization of a Neolithic diet. Methods Quantitative diet reconstruction was achieved through a novel approach consisting of the use of the Bayesian mixing model Food Reconstruction Using Isotopic Transferred Signals (FRUITS) to model isotope measurements from multiple dietary proxies (δ13Ccollagen, δ15Ncollagen, δ13Cbioapatite, δ34Smethione,14Ccollagen). The accuracy of model estimates was verified by comparing the agreement between observed and estimated human dietary radiocarbon reservoir effects. Results Quantitative diet reconstruction estimates confirm that the Ostorf individuals had a high protein intake due to the consumption of fish and terrestrial animal products. However, FRUITS estimates also show that plant foods represented a significant source of calories. Observed and estimated human dietary radiocarbon reservoir effects are in good agreement provided that the aquatic reservoir effect at Lake Ostorf is taken as reference. Conclusions The Ostorf population apparently adopted elements associated with a Neolithic culture but adapted to available local food resources and implemented a subsistence strategy that involved a large proportion of fish and terrestrial meat consumption. This case study exemplifies the diversity of subsistence strategies followed during the Neolithic. Am J Phys Anthropol 158:325–340, 2015. © 2015 Wiley Periodicals, Inc.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".