Exploring the Influence of Neomorphic Gekkotan Paraphalanges on Limb Modularity and Integration
Bibliographic record
Abstract
Digital specializations of geckos are widely associated with their climbing abilities. A recurring feature that has independently emerged within the sister families Gekkonidae and Phyllodactylidae is the presence of neomorphic paraphalanges (PPEs), usually paired, paraxial skeletal structures lying adjacent to interphalangeal and metapodial-phalangeal joints. The incorporation of PPEs into gekkotan autopodia has the potential to modify the modularity and integration of the ancestral limb pattern by affecting information flow among skeletal limb parts. Here we explore the influence of PPEs on limb organization using anatomical networks. We modeled the fore- and hindlimbs in species ancestrally devoid of PPEs (Iguana iguana and Gekko gecko) and paraphalanx-bearing species (Hemidactylus mabouia and Uroplatus fimbriatus). To further clarify the impact of PPEs we also expunged PPEs from paraphalanx-bearing network models. We found that PPEs significantly increase modularity, giving rise to tightly integrated sub-modules along the digits, suggesting functional specialization. Species-specific singularities also emerged, such as the trade-off between the presence of PPEs favoring modularity (along the proximodistal axis) and the interdigital webbing favoring integration (across the lateromedial axis) in the limbs of U. fimbriatus. The PPEs are characterized by low connectivity compared with other skeletal elements; nevertheless, this varies based on their specific location and seemingly reflects developmental constraints. Our results also highlight the importance of the fifth metatarsal in generating a shift in lepidosaurian hindlimb polarity that contrasts with the more symmetrical bauplan of tetrapods. Our findings support extensive modification of the autopodial system in association with the addition of the neomorphic and intriguing PPEs.
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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 | 0.000 | 0.000 |
| 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 | 0.001 |
| 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".