<i>Homo naledi</i> lumbar vertebrae and a new <scp>3D</scp> method to quantify vertebral wedging
Bibliographic record
Abstract
Abstract Objectives In humans and known fossil hominins, lumbar lordosis is produced by vertebral body wedging and other bony and soft tissue features such as the shape of the intervertebral discs. Current techniques for quantifying the wedging of vertebral bodies are limited in utility, especially when analyzing incomplete fossil material. Here, we introduce a 3D method to quantify vertebral body wedging angles that yields the angles between two “best fit” planes in the software GeoMagic Wrap (3D Systems). Materials and Methods To test that this new method is repeatable with existing methods, we measure the wedging of 320 lumbar vertebrae representing 64 modern human individuals. For each vertebra, wedging angles were calculated from linear measurements taken with calipers and compared with estimates generated from the 3D best fit plane method. We also apply the 3D plane method to fossil hominin lumbar vertebrae, including newly described lumbar vertebrae of Homo naledi, the majority of which do not preserve the four landmarks necessary to calculate wedging angles using the traditional approach. Results The results of the two methods are highly and significantly correlated (r2 = 0.98, p < 0.0001). The 3D plane method was successfully applied to nearly all of the fossil hominin specimens included in the study. Discussion The new 3D plane method introduced here is repeatable with the traditional linear measurement method and allows for the estimation of wedging angles in incomplete material. When applied to Homo naledi lumbar vertebrae, similarities to other fossil hominins and modern humans are found.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".