The 'Reverse Engineering' approach to hominin long bone reconstruction: Transforming human evolution studies
Bibliographic record
Abstract
des os longs d'hominines : impact sur les études de l'évolution humaineLloyd Austin Courtenay et Julia Aramendi 1 Our grasp of human evolution faces limitations due to the challenges posed by the discovery of isolated and often incomplete hominin remains in a complex evolutionary context.Over time, paleoanthropologists have developed various techniques for reconstructing these fragments, which can sometimes introduce methodological uncertainties and biases.In this context, we introduce a pioneering approach known as the 'Reverse Engineering' method, which focuses on reconstructing long bones of hominins.We illustrate this methodology using a case study involving the reconstruction of fragmented humeri, radii, femora, and tibiae from Homo naledi.This approach integrates 3D geometric morphometrics and advanced computational, mathematical, and artificial intelligence tools, utilizing complete long bones from modern human and primate reference groups.By combining landmarks and semilandmarks, we have created a database describing morphological variations among anatomically modern humans, chimpanzees, gorillas, and orangutans.For the H. naledi specimens, we initially aligned and mirrored them to establish correspondences with complete modern long bones before landmarking.Subsequently, we employed the 'Reverse Engineering' method to estimate missing landmarks, using geometric morphometric information processed through dimensionality reduction techniques.This enabled us to establish a mathematical relationship between preserved bone portions in each fossil fragment and complete modern long bones, ultimately predicting the morphology of the entire bone corresponding to each fossil fragment.Our pursuit of this mathematical relationship included experimenting with multipleThe 'Reverse Engineering' approach to hominin long bone reconstruction: Trans...
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".