Approaches to Estimating Marine Protein in Human Collagen for Radiocarbon Date Calibration
Bibliographic record
Abstract
Determining the appropriate approach to calibrating radiocarbon dates is challenging when unknown and variable fractions of the carbon sample are derived from terrestrial and marine systems. Uncalibrated dates from a large number of human skeletons from Western Cape and Southern Cape locales, South Africa (n = 187), can be used to explore alternate approaches to the marine carbon correction. The approach that estimates theoretically expected minimum and maximum values for marine carbon (“expected”) is compared to the approach that estimates observed minimum and maximum values (“observed”). Two case studies are explored, wherein skeletons interred together have non-overlapping conventional 14C ages. The case from the Western Cape is explored through carbon isotope values; the case from the Southern Cape uses nitrogen isotope values. In both cases, the approach using observed endpoints yields better date calibration results. Analysis of the large sample shows that mean values for estimated dietary % Marine, as calculated using expected and observed protocols, are significantly different. We conclude that the observed protocol is preferred, and that improved measures of the local marine reservoir (ΔR) are needed for this region.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".