Estimating fossil hominin body mass from cranial variables: An assessment using CT data from modern humans of known body mass
Bibliographic record
Abstract
Body mass estimates are integral to a wide range of inferences in paleoanthropology. Most techniques employ postcranial elements, but predictive equations based on cranial variables have also been developed. Three studies currently provide regression equations for estimating mass from cranial variables, but none of the equations has been tested on samples of known mass. Nor have the equations been compared to each other in terms of performance. Consequently, this study assessed the performance of existing cranial equations using computed tomography scans from a large, documented sample of modern humans of known body mass. Virtual models of the skull were reconstructed and measured using computer software, and the resulting variables were entered into three sets of published regression equations. Estimated and known body masses were then compared. For most equations, prediction errors were high and few individuals were estimated within ±20% of their known mass. Only one equation satisfied the accuracy criteria. In addition, variables that had been previously argued to be good predictors of mass in hominins, including humans, did not estimate mass reliably. These results have important implications for paleoanthropology. In particular, they emphasize the need to develop new equations for estimating fossil hominin body mass from cranial variables.
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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 | 0.006 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.000 | 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".