Variability and the form–function framework in evolutionary biomechanics and human locomotion
Bibliographic record
Abstract
Abstract The form–function conceptual framework, which assumes a strong relationship between the structure of a particular trait and its function, has been crucial for understanding morphological variation and locomotion among extant and fossil species across many disciplines. In biological anthropology, it is the lens through which many important questions and hypotheses have been tackled with respect to relationships between morphology and locomotor kinematics, energetics and performance. However, it is becoming increasingly evident that the morphologies of fossil hominins, apes and humans can confer considerable locomotor diversity and flexibility, and can do so with a range of kinematics depending on soft tissue plasticity and environmental and cultural factors. This complexity is not built into traditional biomechanical or mathematical models of relationships between structure and kinematics or energetics, limiting our interpretation of what bone structure is telling us about behaviour in the past. The nine papers presented in this Special Collection together address some of the challenges thatvariationin the relationship between form and function pose in evolutionary biomechanics, to better characterise the complexity linking structure and function and to provide tools through which we may begin to incorporate some of this complexity into our functional interpretations.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".