“Mind the gap”—Assessing methods for aligning age determination and growth rate in multi‐molar sequences of dietary isotopic data
Bibliographic record
Abstract
OBJECTIVES: Creating multi-tooth sequences of micro-sampled stable isotope (SI) analytical data can help track 20+ years of individual dietary history. Inferences about individual and population level behavioral patterns require cross-calibration of the timing of dietary changes recorded by each tooth. Dentin sections from contemporaneous tissues (eg, in M1 and M2) reflect dietary signature for the time of growth. Contemporary sections should produce similar values, allowing alignment of temporally overlapping portions of teeth into multi-tooth sequences. Published methods for determining the ages of incremental sections do not provide guidance for adjustment when poor alignment between individual tooth sequences is encountered. The primary objective is to address this problem; examine cause(s), assess the effects of the standard growth-model on available age-assessment techniques, and provide a viable solution. METHODS: Investigating difficulty in aligning a 3-molar sequence at Shamanka II, an Early Neolithic (7000-5700 BP) Kitoi hunter-gatherer cemetery in Cis-Baikal, Siberia, we employed 10 age assessment models and 13 variants of 2 published growth rate methods on 3 individuals of different age and sex. RESULTS: At Shamanka II, dentin initiation and/or growth rates were different from the mostly European, reference populations used to create published age-estimation/growth rate models. Initiation ages for M2 and M3 were delayed. Root formation rates were on the rapid end of known development parameters. CONCLUSIONS: Age-assessment methods customized to dentin initiation ages and growth parameters of Siberian populations produced a hybrid growth rate model for dentin section ages and improved alignment for multi-tooth SI sequences over published 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 | 0.064 | 0.155 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".