Intraskeletal isotopic compositions (δ<sup>13</sup>C, δ<sup>15</sup>N) of bone collagen: Nonpathological and pathological variation
Bibliographic record
Abstract
Paleodiet research traditionally interprets differences in collagen isotopic compositions (δ(13) C, δ(15) N) as indicators of dietary distinction even though physiological processes likely play some role in creating variation. This research investigates the degree to which bone collagen δ(13) C and δ(15) N values normally vary within the skeleton and examines the influence of several diseases common to ancient populations on these isotopic compositions. The samples derive from two medieval German cemeteries and one Swiss reference collection and include examples of metabolic disease (rickets/osteomalacia), degenerative joint disease (osteoarthritis), trauma (fracture), infection (osteomyelitis), and inflammation (periostitis). A separate subset of visibly nonpathological skeletal elements from the German collections established normal intraindividual variation. For each disease type, tests compared bone lesion samples to those near and distant to the lesions sites. Results show that normal (nonpathological) skeletons exhibit limited intraskeletal variation in carbon- and nitrogen-isotope ratios, suggesting that sampling of distinct elements is appropriate for paleodiet studies. In contrast, individuals with osteomyelitis, healed fractures, and osteoarthritis exhibit significant intraskeletal differences in isotope values, depending on whether one is comparing lesions to near or to distant sites. Skeletons with periostitis result in significant intraskeletal differences in nitrogen isotope values only, while those with rickets/osteomalacia do not exhibit significant intraskeletal differences. Based on these results, we suggest that paleodiet researchers avoid sampling collagen at or close to lesion sites because the isotope values may be reflecting both altered metabolic processes and differences in diet relative to others in the population.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".