Whole-body variational modularity in the zebrafish: an inside-out story of a model species
Bibliographic record
Abstract
Actinopterygians are the most diversified clade of extant vertebrates. Their impressive morphological disparity bears witness to tremendous ecological diversity. Modularity, the organization of biological systems into quasi-independent anatomical/morphological units, is thought to increase evolvability of organisms and facilitate morphological diversification. Our study aims to quantify patterns of variational modularity in a model actinopterygian, the zebrafish (Danio rerio), using three-dimensional geometric morphometrics on osteological structures isolated from micro-CT scans. A total of 72 landmarks were digitized along cranial and postcranial ossified regions of 30 adult zebrafishes. Two methods were used to test modularity hypotheses, the covariance ratio and the distance matrix approach. We find strong support for two modules, one comprised paired fins and the other comprised median fins, that are best explained by functional properties of subcarangiform swimming. While the skull is tightly integrated with the rest of the body, its intrinsic integration is relatively weak supporting previous findings that the fish skull is a modular structure. Our results provide additional support for the recognition of similar hypotheses of modularity identified based on external morphology in various teleosts, and at least two variational modules are proposed. Thus, our results hint at the possibility that internal and external modularity patterns may be congruent.
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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 | 0.001 |
| 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 | 0.002 |
| Scholarly communication | 0.000 | 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".