Anatomical comparison across heads, fore‐ and hindlimbs in mammals using network models
Bibliographic record
Abstract
Animal body parts evolve with variable degrees of integration that nonetheless yield functional adult phenotypes: but, how? The analysis of modularity with Anatomical Network Analysis (AnNA) is used to quantitatively determine phenotypic modules based on the physical connection among anatomical elements, an approach that is valuable to understand developmental and evolutionary constraints. We created anatomical network models of the head, forelimb, and hindlimb of two taxa considered to represent a 'generalized' eutherian (placental: mouse) and metatherian (marsupial: opossum) anatomical configuration and compared them with our species, which has a derived eutherian configuration. In these models, nodes represent anatomical units and links represent their physical connection. Here, we aimed to identify: (1) the commonalities and differences in modularity between species, (2) whether modules present a potential phylogenetic character, and (3) whether modules preferentially reflect either developmental or functional aspects of anatomy, or a mix of both. We predicted differences between networks of metatherian and eutherian mammals that would best be explained by functional constraints, versus by constraints of development and/or phylogeny. The topology of contacts between bones, muscles, and bones + muscles showed that, among all three species, skeletal networks were more similar than musculoskeletal networks. There was no clear indication that humans and mice are more alike when compared to the opossum overall, even though their musculoskeletal and skeletal networks of fore- and hindlimbs are slightly more similar. Differences were greatest among musculoskeletal networks of heads and next of forelimbs, which showed more variation than hindlimbs, supporting previous anatomical studies indicating that in general the configuration of the hindlimbs changes less across evolutionary history. Most observations regarding the anatomical networks seem to be best explained by function, but an exception is the adult opossum ear ossicles. These ear bones might form an independent module because the incus and malleus are involved in forming a functional primary jaw that enables the neonate to attach to the teat, where this newborn will complete its development. Additionally, the human data show a specialized digit 1 module (thumb/big toe) in both limb types, likely the result of functional and evolutionary pressures, as our ape ancestors had highly movable big toes and thumbs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".