Phylogenetic, functional, and structural components of variation in bone growth rate of amniotes
Bibliographic record
Abstract
The biological features observed in every living organism are the outcome of three sets of factors: historical (inherited by homology), functional (biological adaptation), and structural (properties inherent to the materials with which organs are constructed, and the morphogenetic rules by which they grow). Integrating them should bring satisfactory causal explanations of empirical data. However, little progress has been accomplished in practice toward this goal, because a methodologically efficient tool was lacking. Here we use a new statistical method of variation partitioning to analyze bone growth in amniotes. (1) Historical component. The variation of bone growth rates contains a significant phylogenetic signal, suggesting that the observed patterns are partly the outcome of shared ancestry. (2) Functional causation. High growth rates, although energy costly, may be adaptive (i.e., they may increase survival rates) in taxa showing short growth periods (e.g., birds). In ectothermic amniotes, low resting metabolic rates may limit the maximum possible growth rates. (3) Structural constraint. Whereas soft tissues grow through a multiplicative process, growth of mineralized tissues is accretionary (additive, i.e., mineralization fronts occur only at free surfaces). Bone growth of many amniotes partially circumvents this constraint: it is achieved not only at the external surface of the bone shaft, but also within cavities included in the bone cortex as it grows centrifugally. Our approach contributes to the unification of historicism, functionalism, and structuralism toward a more integrated evolutionary biology.
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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 | 0.002 | 0.001 |
| 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) | 0.000 | 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".