A Macaque Model for the Effects of Hybridization on Body Size
Bibliographic record
Abstract
OBJECTIVES: Genomics research has uncovered recurrent hybridization between hominin species, yet its morphological impact remains understudied. Non-human primate research has suggested a morphological signature of hybrid ancestry, which could be used to identify hybrids in the hominin fossil record. This pattern may include extreme size, heightened variation, and markers of developmental instability, but factors affecting these characteristics are poorly understood. Studies of non-mammalian taxa suggest that extreme morphology is more likely in early-generation hybrids and with a greater parental distance. To understand hybridization in hominins, therefore, we must use appropriate proxy taxa. MATERIALS AND METHODS: Here, we use Chinese × Indian Macaca mulatta hybrids with a comparable divergence time in generations to Homo sapiens/Neanderthals and wide variation in admixture. Measuring limb lengths, body length, and weight, we investigate the relationship between admixture and size/variation. RESULTS: Compared to previous work with more phylogenetically distant primate taxa and a focus on early generation hybrids, we found no evidence of a relationship between admixture and extreme large size, nor with increased size variation. Hybrids in our sample are relatively small but within the range of variation of the smaller parental taxon. CONCLUSIONS: Our results suggest that hybridization between closely related taxa, such as Neanderthals and H. sapiens, may lead to more subtle morphological patterns than previously anticipated. It will be necessary, however, to better understand the factors governing primate hybrid morphology before we can produce robust inferences on how hybridization has affected hominin evolution.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".