Α Rare Morphological Study Concerning the Longest Bone of the Human Anatomy in the Population of the Northern Greece
Bibliographic record
Abstract
BACKGROUND: The femur is one of the most researched bones in the human anatomy and forensic medicine. As the longest bone in the human body, it is well preserved in skeletal remains. The sex estimation of human remains is one of the most important research steps for physical and forensic anthropologists. However, osteometric standards built on unburned human remains and contemporary cremated series are often inadequate for the analysis, frequently resulting in a significant number of misclassifications. METHODS: In our study, we present the anthropometric data from 500 skeletons in Northern Greece, including 232 males and 198 females, as well as 430 of known age. The diameters of the femur were measured as well as the indices of robustness. For the statistical interpretation of the results, we have used the discriminant analysis. RESULTS: From the interpretation of the data, we concluded that all the mean values, diameters and indices of the males were greater compared with those of the females. Also, we concluded that the probability of error is quite high in all cases except the vertical diameter of femur's head, which has an acceptable percentage of error of 14.39% and can be used as a safe criterion for sex identification. CONCLUSION: With the aid of statistics, we came to the conclusion that the vertical diameter of the femur's head is a safe variable for sex estimation in skeletal remains.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 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".