Výpovědní hodnota mineralizace trvalé dentice pro odhad věku u dvou evropských recentních populací.
Bibliographic record
Abstract
Age estimation is a common requirement in forensic, bioarcheological and biomedical practice. This master thesis deals with age estimation based on permanent tooth mineralization according to Demirjian et al. (1973). The research material consisted of orthopantomograms of 716 Czech and 743 French children aged between 4 and 15 years. The purpose of this study was to analyse the suitability of the original French-Canadian standards for age estimation (Demirjian a Goldstein, 1976) and the recent Belgian standards (Willems et al., 2001) in Czech and French population. Another aim of the study was to evaluate the accuracy of the neural network method that represents a completely new approach in data prediction. In order to express the accuracy of estimate we used mean and median of difference between chronological and dental age, and RMS error. Using logistic regression, differences in tooth mineralization between Czech and French population and between girls and boys were also evaluated. Our results indicate that the French-Canadian standards gave a consistent overestimation of dental age compared with chronological age. Mean difference was 0,33 years for Czech children and 0,45 and 0,46 years for French girls and boys, respectively. We found that Willem's method and neural network method were more...
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".