Does age estimated from teeth forming in different early life periods show differential discrepancy with known age?
Bibliographic record
Abstract
OBJECTIVES: The aim of this study is to explore growth discrepancies in the dentition of impoverished children and examine how dental development is impacted by environmental influences throughout childhood, thereby identifying which teeth are more sensitive to the effects of biocultural factors and are consequently less useful to predict age. METHODS: Length measurements of developing teeth (deciduous and permanent) were taken from individuals of known age and sex (n = 61) from the Certosa collection, a 19th century skeletal assemblage representing Italian children of low socioeconomic status. Discrepancies between age estimates based on tooth length and chronological age were calculated, and the accuracy and precision of age prediction between earlier forming teeth and later forming teeth were compared. RESULTS: Deciduous teeth produced more precise dental age estimates (mean age discrepancy -0.092 years), while discrepancies between chronological age and age based on developing permanent dentition were larger (-0.628 years). The difference between these discrepancies in age estimates for deciduous and permanent teeth was significant (p < 0.001), indicating that age prediction from deciduous tooth length is more accurate than age predicted using permanent tooth length. CONCLUSION: An increasing variation and delay in tooth length for age reflects increasing susceptibility to biocultural factors, which impacts tooth growth during the course of childhood. Teeth whose development occurs earlier in life are less variable in their growth and provide more accurate estimations of age as a result.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".