Evaluating the Evolvability of <i>Paranthropus</i> Cranial Morphology in Relation to Feeding Biomechanics
Bibliographic record
Abstract
OBJECTIVE: Although disagreement persists as to the precise nature of the diet of Paranthropus, there is a consensus that the food resources consumed by Paranthropus were in some way mechanically challenging to process (i.e., by being "hard" and/or "tough"). While the highly derived feeding apparatus of Paranthropus likely conferred biomechanical performance advantages while consuming certain types of foods, it may also have limited the ability of these early hominins to respond to selection and evolve rapidly toward new adaptive peaks (i.e., reduced their evolvability). MATERIALS AND METHODS: We employed viability selection modeling to test this hypothesis. Viability selection simulations were performed using Paranthropus boisei (OH 5), Australopithecus afarensis (A.L. 444-2), and Homo habilis (KNM-ER 1813) specimens. We simulated the generation-to-generation evolution of biomechanically informative linear dimensions in a population where an individual's probability of survival (i.e., viability) was determined by its distance to a predetermined adaptive peak. The number of generations required for an evolving population to reach a new adaptive peak was used as a measure of evolvability. RESULTS: The results showed that the mean number of generations from P. boisei to H. habilis was larger than in the reverse direction when modeled using either chimpanzee or human estimates of population variance/covariance. It took longer for P. boisei to evolve toward Au. afarensis than in the reverse direction, but only with the chimpanzee estimates of population variance/covariance. DISCUSSION: The results suggest that P. boisei faced limitations in cranial evolvability, particularly if selection favored a cranial morphology similar to H. habilis.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".