Prominent Methods and Theories in the Estimation of Body Mass from Skeletal Remains
Bibliographic record
Abstract
Estimating body mass from skeletal remains is considered a gap in the creation of a biological profile. Over the last few decades, there have been attempts to fill this gap using different elements from the skeleton. Using various academic databases, a study was done to investigate the prominent methods and theories in body mass estimation. These methods include the use of the femur, the articular surfaces, and the interpretation of musculoskeletal stress markers at the entheses. Calculations using the femur found success in adults most prominently when the cortical area is used. The cortical area provided a percent error margin of 14–22%, with the error decreasing when sex and ancestry-specific equations were used. Musculoskeletal stress markers correlated with heavier body mass in various regions when looking at robusticity. However, these results could not be distinguished between higher body mass individuals and athletic individuals. The articular surface area exhibited no change when body mass is considered, although other features such as osteoarthritis can potentially provide insight into body mass. In addition, subadult femurs were investigated and provided error percentages of 5–7% for juveniles 7 years and younger, and the bi-iliac breadth with long bones can be used for those 15–17 years old with an error margin of 5–8%. These methods exhibit limitations in the demographics of the study, the lack of weight extremely investigated, and various confounding factors. However, these methods and theories in body mass estimations from skeletal remains provide a promising start.
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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 | 0.030 | 0.070 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".